void DataChannelMPI::allGather(std::vector<at::Tensor>& output,
at::Tensor& input, THDGroup group_id) {
const auto& group_pair = _groups.at(group_id);
const auto& comm = group_pair.first;
if (comm == MPI_COMM_NULL)
return;
if (output.size() != group_pair.second.size())
throw std::logic_error("allGather: number of output tensors and group size does not match");
for (auto out_tensor : output)
assertSameSizeAndType(out_tensor, input, "allGather");
auto recv_buffer = _newLikeFlat(output);
auto contig_input = input.contiguous();
MPI_Allgather(
contig_input.data_ptr(), contig_input.numel(), mpi_datatype.at(contig_input.type().scalarType()),
recv_buffer.data_ptr(), contig_input.numel(), mpi_datatype.at(recv_buffer.type().scalarType()),
comm
);
for (size_t i = 0; i < output.size(); ++i)
output[i].copy_(recv_buffer[i]);
}
void dissemble(PFBlock fb)
{
Opcodes opcode;
int rmode,rdata, k = 0;
string name;
Instruction *pi = fb->pstart;
while (pi != end_of_code) {
opcode = (Opcodes)pi->opcode;
// *add 1.2.4 HALT+data is not always a breakpoint!
// (it is used as a NOP + <any useful tag data>)
if (opcode == HALT) {
if (pi->data < MAX_BREAKPOINTS) {
Breakpoint *pb = Breakpoint::from_id(pi->data);
Instruction ai = pb->saved_instruction();
std::cout << "*";
opcode = (Opcodes)ai.opcode;
rmode = ai.rmode; rdata = ai.data;
} else { opcode = NOP; rdata = pi->data; }
} else {
rmode = pi->rmode; rdata = pi->data;
}
name = get_opcode_name(opcode);
std::cout << k++ << ' ' << name << '\t';
if (opcode == CCALL || opcode == CALL || opcode == CALLD || opcode == CALLN) {
FBlock* pfb;
void *data = data_ptr(rdata);
if (opcode == CALLN)
pfb = Builtin::imported_fblock_from_function((void*)((NFBlock *)data)->pfn);
else pfb = PFBlock(data_ptr(rdata));
if (pfb) Function::from_fun_block(pfb)->dump(std::cout);
} else
if (opcode == JSWITCH) {
int *swb = (int *)data_ptr(rdata);
int sz = *swb++;
int def = *swb++;
std::cout << '(' << sz << ',' << def << ") ";
for (int i = 0; i < sz; i++) std::cout << *swb++ << ' ' << *swb++ << ' ';
}
else
if (opcode == TOSD || opcode == TPODS) {
PClass pc = *(PClass *)data_ptr(rdata);
std::cout << pc->name();
}
else {
if (rmode)
switch(rmode) {
case DIRECT: std::cout << "D "; break;
case SREL: std::cout << "R "; break;
case OREL: std::cout << "S "; break;
}
if (rdata != 0) std::cout << rdata;
}
std::cout << std::endl;
if (opcode == RET || opcode == RETI || opcode == RETD) break;
pi++;
}
}
void* kma_malloc(kma_size_t size)
{
if(size >= PAGE_SIZE)
return NULL;
if(entry_page == NULL)
init_first_page();
kma_frame* current = (kma_frame*)entry_page->ptr;
//go until last in the list or we find one that fits
bool fits = (current->occupied == FREE && frame_size(current) >= size);
while(current->last == NOT_LAST && !fits){
current = current->next;
fits = (current->occupied == FREE && frame_size(current) >= size);
}
void* ret_addr;
//if it fits...
if(fits){
allocate_frame(current,size);
ret_addr = data_ptr(current);
//if not...
}else{
//if nothing in the resource map fits, we need to allocate a new page
//ifwe are here, current should be last
kma_page_t* new_page = get_page();
void* next = ((char*) new_page->ptr) + new_page->size;
kma_frame* new_frame = write_new_frame(new_page->ptr, new_page, current, next, FREE, LAST);
allocate_frame(new_frame,size);
current->last = NOT_LAST;
ret_addr = data_ptr(new_frame);
}
//print_debug();
return ret_addr;
}
std::vector<BackendChunk*> SimpleBackend::Create(const std::vector<BackendChunk*>& input,
const std::vector<Scale>& result_sizes, std::shared_ptr<ComputeFn> fn) {
auto current_device_id = MinervaSystem::Instance().current_device_id();
std::vector<BackendChunk*> result_chunks;
Task* task = new Task();
for (auto i : input) {
auto c = CHECK_NOTNULL(dynamic_cast<SimpleChunk*>(i));
task->inputs.emplace_back(c->data(), 0);
}
for (auto s : result_sizes) {
auto data_id = MinervaSystem::Instance().GenerateDataId();
std::shared_ptr<PhysicalData> data_ptr( new PhysicalData(s, current_device_id, data_id),
[&] (PhysicalData* d) { device_manager_.FreeData(d->data_id); delete d; } );
SimpleChunk* o = new SimpleChunk(data_ptr);
result_chunks.emplace_back(o);
task->outputs.emplace_back(o->data(), 0);
}
task->op = PhysicalOp{fn, current_device_id};
task->id = 0;
DLOG(INFO) << "executing task name=" << fn->Name() << " to device #" << current_device_id;
// wait for finish
unique_lock<mutex> ul(finish_mutex_);
device_manager_.GetDevice(current_device_id)->PushTask(task);
finished_flag_ = false;
while(! finished_flag_) {
finish_cond_.wait(ul);
}
return result_chunks;
}
bool
pic_data_p(pic_state *pic, pic_value obj, const pic_data_type *type)
{
if (pic_type(pic, obj) != PIC_TYPE_DATA) {
return false;
}
return type == NULL || data_ptr(pic, obj)->type == type;
}
static Tensor new_from_data(const Type & type, int device, PyObject *data) {
if (PySequence_Check(data)) {
return new_from_sequence(type, device, data);
} else {
// could use scalarTensor but using store_scalar for consistency with the sequence path;
// this has stricter checking (i.e. a floating-point number passed to an integral type will error).
auto tensor = type.tensor({});
torch::utils::store_scalar((char*)tensor.data_ptr(), type.scalarType(), data);
return tensor;
}
}
std::vector<BackendChunk*> SimpleBackend::Create(const std::vector<BackendChunk*>& input,
const std::vector<Scale>& result_sizes, std::shared_ptr<ComputeFn> fn) {
auto current_device_id = MinervaSystem::Instance().current_device_id();
std::vector<BackendChunk*> result_chunks;
Task* task = new Task();
task->light = true;
//printf("Backend collecting task inputs\n");
for (auto i : input) {
auto c = CHECK_NOTNULL(dynamic_cast<SimpleChunk*>(i));
task->inputs.emplace_back(c->data(), 0);
}
//printf("Backend collecting %lu task outputs\n",result_sizes.size());
for (auto s : result_sizes) {
auto data_id = MinervaSystem::Instance().GenerateDataId();
#ifdef HAS_MPI
int current_device_id = MinervaSystem::Instance().current_device_id();
int currentrank = MinervaSystem::Instance().device_manager().GetDevice(current_device_id)->rank();
//printf("[%d] Backend Creating new PhysicalData on rank %d\n",MinervaSystem::Instance().rank(),currentrank);
std::shared_ptr<PhysicalData> data_ptr( new PhysicalData(s, currentrank, current_device_id, data_id),
[&] (PhysicalData* d) { device_manager_.FreeData(d->data_id); delete d; } );
#else
std::shared_ptr<PhysicalData> data_ptr( new PhysicalData(s, current_device_id, data_id),
[&] (PhysicalData* d) { device_manager_.FreeData(d->data_id); delete d; } );
#endif
SimpleChunk* o = new SimpleChunk(data_ptr);
result_chunks.emplace_back(o);
task->outputs.emplace_back(o->data(), 0);
}
task->op = PhysicalOp{fn, current_device_id};
task->id = MinervaSystem::Instance().GenerateTaskId();
DLOG(INFO) << "executing task name=" << fn->Name() << " to device #" << current_device_id;
// wait for finish
// unique_lock<mutex> ul(finish_mutex_);
// finished_flag_.store(false);;
device_manager_.GetDevice(current_device_id)->PushTask(task);
// while(! finished_flag_.load()) {
// finish_cond_.wait(ul);
// }
return result_chunks;
}
//C[x] += A[x]*B[x]
//(if not 4-dimensional, then indexing [x] is ignored (e.g. for weight matrices))
void affine_y_x(int x_A, Ndarray* A, int x_B, Ndarray* B,
int x_C, /*out*/Ndarray* C, bool transpose_A = false, bool transpose_B = false) {
const float* data_A = data_ptr(A, x_A);
const float* data_B = data_ptr(B, x_B);
float* data_C = data_ptr(C, x_C);
int A_dim[2], B_dim[2];
lastTwoDims(A, A_dim);
lastTwoDims(B, B_dim);
int ldB = B_dim[1];
int ldA = A_dim[1];
char transA = transpose_A ? 'T' : 'N';
char transB = transpose_B ? 'T' : 'N';
if (transpose_A)
std::swap(A_dim[0], A_dim[1]);
if (transpose_B)
std::swap(B_dim[0], B_dim[1]);
const float alpha = 1;
const float beta = 1;
Ndarray_sgemm(transB, transA, B_dim[1], A_dim[0], A_dim[1], &alpha, data_B, ldB,
data_A, ldA, &beta, data_C, B_dim[1]);
}
static Tensor new_from_sequence(ScalarType scalarType, PyObject* data) {
if (!PySequence_Check(data)) {
throw TypeError("new(): data must be a sequence (got %s)", Py_TYPE(data)->tp_name);
}
if (THPUtils_checkString(data)) {
throw TypeError("new(): invalid data type '%s'", Py_TYPE(data)->tp_name);
}
#ifdef WITH_NUMPY
if (PyArray_Check(data)) {
return autograd::make_variable(tensor_from_numpy(data), false);
}
#endif
auto sizes = compute_sizes(data);
auto tensor = autograd::make_variable(CPU(scalarType).tensor(sizes), false);
recursive_store(
(char*)tensor.data_ptr(), tensor.sizes(), tensor.strides(), 0,
scalarType, tensor.type().elementSizeInBytes(), data);
return tensor;
}
/**
* Select histogram block i, store to ihist.
*
* @param ihist histogram i
* @param i histogram block index
* @param j histogram component index (select only a component);
* -1 to select complete block
* @return O_K if ok, NOT_EXEC if not executed
*/
INT16 CGEN_PUBLIC CHistogram::SelectBlock(CData* ihist, INT32 i, INT32 j)
{
INT32 l, rl;
if (CheckHisto() != O_K)
return (NOT_EXEC);
if (ihist == NULL)
return (NOT_EXEC);
if (i < 0 || i >= m_nhist)
return (NOT_EXEC);
rl = BytesPerBlock();
data_reset (ihist);
if (j < 0)
{
data_scopy (m_hist, ihist);
data_arr_alloc (ihist,m_bins);
copy_data_descr (m_hist, ihist);
set_data_nblock (ihist, 1);
dl_memmove ( (char*)data_ptr(ihist),
(char*)xaddr(m_hist,i*m_bins,0), rl);
return (O_K);
}
if (j > -1 && j < data_dim(m_hist))
{
comp_mdef (ihist,1,T_DOUBLE);
data_arr_alloc (ihist,m_bins);
copy_comp_text (m_hist, j, ihist, 0, 1);
copy_data_descr(m_hist, ihist);
set_data_nblock (ihist, 1);
for (l = 0; l < m_bins; l++)
dstore (dfetch(m_hist,i*m_bins+l,j), ihist,l,0);
return (O_K);
}
return (NOT_EXEC);
}
void DataChannelMPI::gather(std::vector<at::Tensor>& output,
at::Tensor& input, rank_type dst_rank,
THDGroup group_id) {
const auto& group_pair = _groups.at(group_id);
const auto& comm = group_pair.first;
if (comm == MPI_COMM_NULL)
return;
at::Tensor recv_buffer;
void *recvbuf = nullptr;
if (_rank != dst_rank) {
if (output.size() > 0)
throw std::logic_error("gather: number of input tensors should be 0 for non root");
} else {
if (output.size() != group_pair.second.size())
throw std::logic_error("gather: number of output tensors and group size does not match");
for (auto out_tensor : output)
assertSameSizeAndType(out_tensor, input, "gather");
recv_buffer = _newLikeFlat(output);
recvbuf = recv_buffer.data_ptr();
}
rank_type group_dst_rank = group_pair.second.mustGetGroupRank(dst_rank);
auto contig_input = input.contiguous();
MPI_Gather(
contig_input.data_ptr(), input.numel(), mpi_datatype.at(input.type().scalarType()),
recvbuf, input.numel(), mpi_datatype.at(input.type().scalarType()),
group_dst_rank, comm
);
// NOTE: this is a no-op in all processes except dst_rank
for (size_t i = 0; i < output.size(); ++i)
output[i].copy_(recv_buffer[i]);
}
const unsigned char * tissuestack::imaging::DicomFileWrapper::getData()
{
// the dicom image
std::unique_ptr<DicomImage> dcmTmp(new DicomImage(this->_file_name.c_str(), CIF_AcrNemaCompatibility));
unsigned long data_size = dcmTmp->getOutputDataSize(
(this->getAllocatedBits() <= 8 || this->isColor()) ? 8 : 16);
unsigned long image_size = this->getWidth() * this->getHeight();
if (data_size == 0)
data_size = image_size * (this->getAllocatedBits() / 2);
if (image_size == 0)
image_size = data_size;
const unsigned long finished_image_size = image_size * 3;
// we converge on an RGB format 8 bit per channel which is what our raw format is
std::unique_ptr<unsigned char[]> data_ptr(new unsigned char[finished_image_size]);
double min = pow(2, this->getAllocatedBits())-1;
double max = 0;
dcmTmp->getMinMaxValues(min,max);
int two_power8 = static_cast<int>(pow(2, 8));
int two_power_16 = static_cast<int>(pow(2, 16));
if (this->isColor()) // COLOR
{
if (dcmTmp->getOutputData(data_ptr.get(), data_size, 8, 0, 0) <= 0)
return nullptr;
return data_ptr.release();
}
if (this->getAllocatedBits() <= 8) // 8 BIT
{
if (this->containsSignedData()) // SIGNED
{
std::unique_ptr<char[]> tmp_ptr(new char[image_size]);
if (dcmTmp->getOutputData(tmp_ptr.get(), data_size, 8, 0, 0) <= 0)
return nullptr;
// convert to unsigned
min = pow(2, this->getAllocatedBits())-1;
max = 0;
for (unsigned long i=0;i<image_size;i++)
{
const unsigned long rgbOffset = i * 3;
const unsigned char val =
static_cast<unsigned char>(static_cast<int>(tmp_ptr.get()[i]) + (two_power8 / 2));
data_ptr.get()[rgbOffset] = data_ptr.get()[rgbOffset+1] = data_ptr.get()[rgbOffset+2] = val;
if (val < min)
min = val;
if (val > max)
max = val;
}
// now that we have min and max ... fit to contrast range linearly
for (unsigned long i=0;i<finished_image_size;i++)
data_ptr.get()[i] =
static_cast<unsigned char>(((static_cast<double>(data_ptr.get()[i])-min) * (two_power8-1)) / (max-min));
// return
return data_ptr.release();
}
// UNSIGNED
std::unique_ptr<unsigned char[]> tmp_ptr(new unsigned char[image_size]);
if (dcmTmp->getOutputData(tmp_ptr.get(), data_size, 8, 0, 0) <= 0)
return nullptr;
// make RGB data
for (unsigned long i=0;i<image_size;i++)
{
const unsigned long rgbOffset = i * 3;
data_ptr.get()[rgbOffset] =
data_ptr.get()[rgbOffset+1] =
data_ptr.get()[rgbOffset+2] = tmp_ptr.get()[i];
}
return data_ptr.release();
}
if (this->getAllocatedBits() > 8) // 12/16 BITS
{
std::unique_ptr<unsigned short[]> tmp_16bit_ptr(new unsigned short[finished_image_size]);
if (this->containsSignedData()) // SIGNED
{
std::unique_ptr<short[]> tmp_ptr(new short[image_size]);
if (dcmTmp->getOutputData(tmp_ptr.get(), data_size, 16, 0, 0) <= 0)
return nullptr;
// convert to unsigned
min = pow(2, this->getAllocatedBits())-1;
max = 0;
for (unsigned long i=0;i<image_size;i++)
{
const unsigned long rgbOffset = i * 3;
const unsigned short val =
static_cast<unsigned short>(static_cast<int>(tmp_ptr.get()[i]) + (two_power_16 / 2));
tmp_16bit_ptr.get()[rgbOffset] = tmp_16bit_ptr.get()[rgbOffset+1] = tmp_16bit_ptr.get()[rgbOffset+2] = val;
if (val < min)
min = val;
if (val > max)
max = val;
}
// now that we have min and max ... fit to contrast range linearly to make it 8 bit
for (unsigned long i=0;i<finished_image_size;i++)
data_ptr.get()[i] =
static_cast<unsigned char>(((static_cast<double>(tmp_16bit_ptr.get()[i])-min) * (two_power8-1)) / (max-min));
return data_ptr.release();
}
// UNSIGNED
if (dcmTmp->getOutputData(tmp_16bit_ptr.get(), data_size, 16, 0, 0) <= 0)
return nullptr;
// turn 16 bit into 8 bit
for (unsigned long i=0;i<image_size;i++)
{
const unsigned long rgbOffset = i * 3;
data_ptr.get()[rgbOffset] = data_ptr.get()[rgbOffset+1] = data_ptr.get()[rgbOffset+2] =
static_cast<unsigned char>((static_cast<double>(tmp_16bit_ptr.get()[i]) * (two_power8-1)) / (two_power_16-1));
}
}
return data_ptr.release();
}
at::Tensor mkldnn_convolution(
const at::Tensor& input, const at::Tensor& weight, const at::Tensor& bias,
IntList padding, IntList stride, IntList dilation)
{
auto output = input.type().tensor(conv_output_size(
input.sizes(), weight.sizes(), padding, stride, dilation));
auto cpu_engine = CpuEngine::Instance().get_engine();
int32_t n = input.size(0);
int32_t ic = input.size(1);
int32_t ih = input.size(2);
int32_t iw = input.size(3);
int32_t oc = output.size(1);
int32_t oh = output.size(2);
int32_t ow = output.size(3);
int32_t kh = weight.size(2);
int32_t kw = weight.size(3);
int32_t sh = stride[0];
int32_t sw = stride[1];
int32_t ph = padding[0];
int32_t pw = padding[1];
auto data_t = memory::data_type::f32;
auto format_any = memory::format::any;
auto format_nchw = memory::format::nchw;
auto format_oihw = memory::format::oihw;
auto format_x = memory::format::x;
memory::dims input_tz = {n, ic, ih, iw};
memory::dims weight_tz = {oc, ic, kh, kw};
memory::dims bias_tz = {oc};
memory::dims output_tz = {n, oc, oh, ow};
memory::dims _stride = {sh, sw};
memory::dims _padding = {ph, pw};
auto input_md = memory::desc({input_tz}, data_t, format_any);
auto weight_md = memory::desc({weight_tz}, data_t, format_any);
auto bias_md = memory::desc({bias_tz}, data_t, format_any);
auto output_md = memory::desc({output_tz}, data_t, format_any);
std::shared_ptr<convolution_forward::desc> conv_forward_desc;
if (bias.defined()) {
conv_forward_desc.reset(new convolution_forward::desc(prop_kind::forward,
convolution_direct, input_md, weight_md, bias_md, output_md,
_stride, _padding, _padding, padding_kind::zero));
} else {
conv_forward_desc.reset(new convolution_forward::desc(prop_kind::forward,
convolution_direct, input_md, weight_md, output_md,
_stride, _padding, _padding, padding_kind::zero));
}
std::shared_ptr<convolution_forward::primitive_desc> conv_forward_pd;
conv_forward_pd.reset(new convolution_forward::primitive_desc(
*conv_forward_desc, cpu_engine));
auto input_usr_memory = memory({{{input_tz}, data_t, format_nchw}, cpu_engine},
input.data_ptr());
auto weight_usr_memory = memory({{{weight_tz}, data_t, format_oihw}, cpu_engine},
weight.data_ptr());
auto output_usr_memory = memory({{{output_tz}, data_t, format_nchw}, cpu_engine},
output.data_ptr());
std::vector<primitive> net;
auto input_pd = conv_forward_pd->src_primitive_desc();
auto input_memory = input_usr_memory;
if (input_usr_memory.get_primitive_desc() != memory::primitive_desc(input_pd)) {
input_memory = memory(input_pd);
net.push_back(reorder(input_usr_memory, input_memory));
}
auto weight_pd = conv_forward_pd->weights_primitive_desc();
auto weight_memory = weight_usr_memory;
if (weight_usr_memory.get_primitive_desc() != memory::primitive_desc(weight_pd)) {
weight_memory = memory(weight_pd);
net.push_back(reorder(weight_usr_memory, weight_memory));
}
auto output_pd = conv_forward_pd->dst_primitive_desc();
auto output_memory = output_usr_memory;
if (output_usr_memory.get_primitive_desc() != memory::primitive_desc(output_pd)) {
output_memory = memory(output_pd);
}
std::shared_ptr<convolution_forward> conv_forward;
std::shared_ptr<memory> bias_usr_memory;
if (bias.defined()) {
bias_usr_memory.reset(new memory({{{bias_tz}, data_t, format_x}, cpu_engine},
bias.data_ptr()));
conv_forward.reset(new convolution_forward(*conv_forward_pd, input_memory,
weight_memory, *bias_usr_memory, output_memory));
} else {
conv_forward.reset(new convolution_forward(*conv_forward_pd, input_memory,
weight_memory, output_memory));
}
net.push_back(*conv_forward);
if (output_memory != output_usr_memory) {
net.push_back(reorder(output_memory, output_usr_memory));
}
Stream::Instance().get_stream().submit(net);
return output;
}
// NB: CuDNN only implements the backward algorithm for batchnorm
// in training mode (evaluation mode batchnorm has a different algorithm),
// which is why this doesn't accept a 'training' parameter.
std::tuple<Tensor, Tensor, Tensor> cudnn_batch_norm_backward(
const Tensor& input_t, const Tensor& grad_output_t, const Tensor& weight_t,
// Unused: but we require them to be passed so that double backwards
// has access
const Tensor& running_mean, const Tensor& running_var,
const Tensor& save_mean_t, const Tensor& save_var_t,
double epsilon)
{
TensorArg input{ input_t, "input", 1 },
grad_output{ grad_output_t, "grad_output", 2 },
weight{ weight_t, "weight", 3 },
save_mean{ save_mean_t, "save_mean", 4 },
save_var{ save_var_t, "save_var", 5 };
CheckedFrom c = "cudnn_batch_norm_backward";
setCuDNNStreamToCurrent();
checkAllDefined(c, {input, grad_output, weight, save_mean, save_var});
checkAllSameGPU(c, {input, grad_output, weight, save_mean, save_var});
if (input->type().scalarType() == ScalarType::Half) {
checkScalarType(c, weight, ScalarType::Float);
} else {
checkAllSameType(c, {input, weight});
}
checkAllSameType(c, {input, grad_output});
checkAllSameType(c, {weight, save_mean, save_var});
// TODO: is weight required to be contiguous?
checkAllContiguous(c, {input, grad_output, save_mean, save_var});
checkDimRange(c, input, 2, 6 /* exclusive */);
checkSameSize(c, input, grad_output);
auto num_features = input->size(1);
for (auto t : {weight, save_mean, save_var}) {
checkNumel(c, t, num_features);
}
cudnnBatchNormMode_t mode;
if (input->dim() == 2) {
mode = CUDNN_BATCHNORM_PER_ACTIVATION;
} else {
#if CUDNN_VERSION >= 7003
mode = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
#else
mode = CUDNN_BATCHNORM_SPATIAL;
#endif
}
auto grad_input_t = input->type().tensor(input->sizes());
auto grad_weight_t = weight->type().tensor(weight->sizes());
auto grad_bias_t = weight->type().tensor(weight->sizes());
auto handle = getCudnnHandle();
auto dataType = getCudnnDataType(*input);
TensorDescriptor idesc{ *input, 4 }; // input, output, grad_output descriptor
TensorDescriptor wdesc{ expandScale(*weight, input->dim()), 4 }; // descriptor for weight, bias, save_mean, etc.
Constant one(dataType, 1);
Constant zero(dataType, 0);
CUDNN_CHECK(cudnnBatchNormalizationBackward(
handle, mode, &one, &zero, &one, &zero,
idesc.desc(), input->data_ptr(),
idesc.desc(), grad_output->data_ptr(),
idesc.desc(), grad_input_t.data_ptr(),
wdesc.desc(), weight->data_ptr(),
grad_weight_t.data_ptr(),
grad_bias_t.data_ptr(),
epsilon,
save_mean->data_ptr(),
save_var->data_ptr()));
return std::tuple<Tensor,Tensor,Tensor>{grad_input_t, grad_weight_t, grad_bias_t};
}
void *
pic_data(pic_state *PIC_UNUSED(pic), pic_value data)
{
return data_ptr(pic, data)->data;
}
/**
* Update histogram data st by data records from x
*
* Uses the followong fields of class CVh:
* hist - histogram data (must be double, may be empty)
* minp - minima-maxima (must be double)
* icomp - index component (may be -1, if not index in data x)
* nind - index number (only for initialization required)
* m_bins - histogram resolution (only for initialization required)
* indexlist - index list of vectors
*
* if index list cannot be created, index indep. histogram is made:
* - no label, no index comp. or no label file
* - label, but no ltab
* mode - 1 constant data interval (minm->dim == 1,
* minm->nvec == 1)
* 2 component spec. quantization (minm->nvec == 2)
* 3 index - comp. spec. quantization (minm->nvec > 2)
*
* @param x Feature vector sequence
* @param wv Weighting vector sequence (may be NULL)
* @return O_K if ok or NOT_EXEC if not executed
*/
INT16 CGEN_PUBLIC CHistogram::UpdateHist(CData* x, CData* wv)
{
INT32 t, il, wdim, T;
FLOAT64 cw;
FLOAT64 *xp, *wvp, *p_mm;
INT16 lflag, flag_w, mflag;
INT32 *qx;
INT16 ierr;
IDENTIFY ("CVh::UpdateHist");
/* Check input data */
if (data_empty(x) == TRUE)
return IERROR(this,HIS_NOINP,0,0,0);
if (data_ndim(x) == 0)
return IERROR(this,HIS_NODAT,0,0,0);
T = data_nrec(x);
m_count = 0;
/* Check index list data */
lflag = 0;
if (data_empty(m_indexlist) != TRUE)
lflag = 1;
/* Use minp data */
SetupHmode();
if (m_hmode <= 0)
return NOT_EXEC;
p_mm = (FLOAT64*) data_ptr(m_minmax);
/* Prepare histogram array */
Prepare(x, wv);
if ((ierr = CheckConsist()) != O_K)
return NOT_EXEC;
/* Check weight data */
flag_w = 0;
wvp = NULL;
wdim = data_ndim(wv);
if (data_empty(wv) != TRUE && lflag == 0)
{
if (wdim < m_nhist)
return (NOT_EXEC);
flag_w = 1;
wvp = (FLOAT64*) dl_calloc (wdim,sizeof(FLOAT64),"UpdateHist");
}
mflag = 0;
if (data_empty(wv) != TRUE && lflag == 1)
{
if (wdim == 1)
mflag = 1;
}
if (flag_w == 1 || mflag == 1)
if (data_nrec(wv) < T)
{
T = data_nrec(wv);
printf("histogram update: less weights than data ... only %d records", T);
}
/* Aux. arrays */
qx = (INT32*) dl_calloc(m_hdim+2, sizeof(INT32), "UpdateHist");
xp = (FLOAT64*) dl_calloc(m_hdim+2, sizeof(FLOAT64),"UpdateHist");
/* Hard update, index driven */
cw = 1.;
if (flag_w == 0)
{
il = 0;
for (t = 0; t < T; t++)
{
dvec_fetch(xp, x, t, m_hdim, m_icomp);
if (lflag != 0)
il = (INT32) dfetch(m_indexlist,t,0);
if (il > -1 && il < m_nhist)
{
m_count++;
if (mflag == 1)
cw = dfetch(wv,t,0);
QuantVec(xp, qx, p_mm, il);
IncrementHisto(qx, cw, il);
}
}
}
/* Soft updating weighted by w-array */
if (flag_w == 1)
{
for (t = 0; t < T; t++)
{
dvec_fetch(wvp,wv,t,wdim,-1);
dvec_fetch(xp,x,t,m_hdim,m_icomp);
for (il = 0; il < m_nhist; il++)
{
m_count++;
QuantVec(xp, qx, p_mm, il);
IncrementHisto(qx, wvp[il], il);
}
}
}
/* Ending activities */dl_free(qx);
dl_free(xp);
if (flag_w == 1)
dl_free (wvp);
return O_K;
}
double stfnum::lmFit( const Vector_double& data, double dt,
const stfnum::storedFunc& fitFunc, const Vector_double& opts,
bool use_scaling,
Vector_double& p, std::string& info, int& warning )
{
// Basic range checking:
if (fitFunc.pInfo.size()!=p.size()) {
std::string msg("Error in stfnum::lmFit()\n"
"function parameters (p_fit) and parameters entered (p) have different sizes");
throw std::runtime_error(msg);
}
if ( opts.size() != 6 ) {
std::string msg("Error in stfnum::lmFit()\n"
"wrong number of options");
throw std::runtime_error(msg);
}
bool constrained = false;
std::vector< double > constrains_lm_lb( fitFunc.pInfo.size() );
std::vector< double > constrains_lm_ub( fitFunc.pInfo.size() );
bool can_scale = use_scaling;
for ( unsigned n_p=0; n_p < fitFunc.pInfo.size(); ++n_p ) {
if ( fitFunc.pInfo[n_p].constrained ) {
constrained = true;
constrains_lm_lb[n_p] = fitFunc.pInfo[n_p].constr_lb;
constrains_lm_ub[n_p] = fitFunc.pInfo[n_p].constr_ub;
} else {
constrains_lm_lb[n_p] = -DBL_MAX;
constrains_lm_ub[n_p] = DBL_MAX;
}
if ( can_scale ) {
if (fitFunc.pInfo[n_p].scale == stfnum::noscale) {
can_scale = false;
}
}
}
// Store the functions at global scope:
saveFunc(fitFunc.func);
saveJac(fitFunc.jac);
double info_id[LM_INFO_SZ];
Vector_double data_ptr(data);
Vector_double xyscale(4);
if (can_scale) {
xyscale = get_scale(data_ptr, dt);
}
// The parameters need to be separated into two parts:
// Those that are to be fitted and those that the client wants
// to keep constant. Since there is no native support to
// do so in Lourakis' routines, the workaround is a little
// tricky, making (ab)use of the *void pointer:
// number of parameters that need to be fitted:
int n_fitted=0;
for ( unsigned n_p=0; n_p < fitFunc.pInfo.size(); ++n_p ) {
n_fitted += fitFunc.pInfo[n_p].toFit;
}
// parameters that need to be fitted:
Vector_double p_toFit(n_fitted);
std::deque<bool> p_fit_bool( fitFunc.pInfo.size() );
// parameters that are held constant:
Vector_double p_const( fitFunc.pInfo.size()-n_fitted );
for ( unsigned n_p=0, n_c=0, n_f=0; n_p < fitFunc.pInfo.size(); ++n_p ) {
if (fitFunc.pInfo[n_p].toFit) {
p_toFit[n_f++] = p[n_p];
if (can_scale) {
p_toFit[n_f-1] = fitFunc.pInfo[n_p].scale(p_toFit[n_f-1], xyscale[0],
xyscale[1], xyscale[2], xyscale[3]);
}
} else {
p_const[n_c++] = p[n_p];
if (can_scale) {
p_const[n_c-1] = fitFunc.pInfo[n_p].scale(p_const[n_c-1], xyscale[0],
xyscale[1], xyscale[2], xyscale[3]);
}
}
p_fit_bool[n_p] = fitFunc.pInfo[n_p].toFit;
}
// size * dt_new = 1 -> dt_new = 1.0/size
double dt_finfo = dt;
if (can_scale)
dt_finfo = 1.0/data_ptr.size();
fitInfo fInfo( p_fit_bool, p_const, dt_finfo );
// make l-value of opts:
Vector_double opts_l(5);
for (std::size_t n=0; n < 4; ++n) opts_l[n] = opts[n];
opts_l[4] = -1e-6;
int it = 0;
if (p_toFit.size()!=0 && data_ptr.size()!=0) {
double old_info_id[LM_INFO_SZ];
// initialize with initial parameter guess:
Vector_double old_p_toFit(p_toFit);
#ifdef _DEBUG
std::ostringstream optsMsg;
optsMsg << "\nopts: ";
for (std::size_t n_p=0; n_p < opts.size(); ++n_p)
optsMsg << opts[n_p] << "\t";
optsMsg << "\n" << "data_ptr[" << data_ptr.size()-1 << "]=" << data_ptr[data_ptr.size()-1] << "\n";
optsMsg << "constrains_lm_lb: ";
for (std::size_t n_p=0; n_p < constrains_lm_lb.size(); ++n_p)
optsMsg << constrains_lm_lb[n_p] << "\t";
optsMsg << "\n" << "constrains_lm_ub: ";
for (std::size_t n_p=0; n_p < constrains_lm_ub.size(); ++n_p)
optsMsg << constrains_lm_ub[n_p] << "\t";
optsMsg << "\n\n";
std::cout << optsMsg;
#endif
while ( 1 ) {
#ifdef _DEBUG
std::ostringstream paramMsg;
paramMsg << "Pass: "******"\t";
paramMsg << "p_toFit: ";
for (std::size_t n_p=0; n_p < p_toFit.size(); ++n_p)
paramMsg << p_toFit[n_p] << "\t";
paramMsg << "\n";
std::cout << paramMsg.str().c_str();
#endif
if ( !fitFunc.hasJac ) {
if ( !constrained ) {
dlevmar_dif( c_func_lour, &p_toFit[0], &data_ptr[0], n_fitted,
(int)data.size(), (int)opts[4], &opts_l[0], info_id,
NULL, NULL, &fInfo );
} else {
dlevmar_bc_dif( c_func_lour, &p_toFit[0], &data_ptr[0], n_fitted,
(int)data.size(), &constrains_lm_lb[0], &constrains_lm_ub[0], NULL,
(int)opts[4], &opts_l[0], info_id, NULL, NULL, &fInfo );
}
} else {
if ( !constrained ) {
dlevmar_der( c_func_lour, c_jac_lour, &p_toFit[0], &data_ptr[0],
n_fitted, (int)data.size(), (int)opts[4], &opts_l[0], info_id,
NULL, NULL, &fInfo );
} else {
dlevmar_bc_der( c_func_lour, c_jac_lour, &p_toFit[0],
&data_ptr[0], n_fitted, (int)data.size(), &constrains_lm_lb[0],
&constrains_lm_ub[0], NULL, (int)opts[4], &opts_l[0], info_id,
NULL, NULL, &fInfo );
}
}
it++;
if ( info_id[1] != info_id[1] ) {
// restore previous parameters if new chisqr is NaN:
p_toFit = old_p_toFit;
} else {
double dchisqr = (info_id[0] - info_id[1]) / info_id[1]; // (old chisqr - new chisqr) / new_chisqr
if ( dchisqr < 0 ) {
// restore previous results and exit if new chisqr is larger:
for ( int n_i = 0; n_i < LM_INFO_SZ; ++n_i ) info_id[n_i] = old_info_id[n_i];
p_toFit = old_p_toFit;
break;
}
if ( dchisqr < 1e-5 ) {
// Keep current results and exit if change in chisqr is below threshold
break;
}
// otherwise, store results and continue iterating:
for ( int n_i = 0; n_i < LM_INFO_SZ; ++n_i ) old_info_id[n_i] = info_id[n_i];
old_p_toFit = p_toFit;
}
if ( it >= opts[5] )
// Exit if maximal number of iterations is reached
break;
// decrease initial step size for next iteration:
opts_l[0] *= 1e-4;
}
} else {
std::runtime_error e("Array of size zero in lmFit");
throw e;
}
// copy back the fitted parameters to p:
for ( unsigned n_p=0, n_f=0, n_c=0; n_p<fitFunc.pInfo.size(); ++n_p ) {
if (fitFunc.pInfo[n_p].toFit) {
p[n_p] = p_toFit[n_f++];
} else {
p[n_p] = p_const[n_c++];
}
if (can_scale) {
p[n_p] = fitFunc.pInfo[n_p].unscale(p[n_p], xyscale[0],
xyscale[1], xyscale[2], xyscale[3]);
}
}
std::ostringstream str_info;
str_info << "Passes: " << it;
str_info << "\nIterations during last pass: "******"\nStopping reason during last pass:"******"\nStopped by small gradient of squared error.";
warning = 0;
break;
case 2:
str_info << "\nStopped by small rel. parameter change.";
warning = 0;
break;
case 3:
str_info << "\nReached max. number of iterations. Restart\n"
<< "with smarter initial parameters and / or with\n"
<< "increased initial scaling factor and / or with\n"
<< "increased max. number of iterations.";
warning = 3;
break;
case 4:
str_info << "\nSingular matrix. Restart from current parameters\n"
<< "with increased initial scaling factor.";
warning = 4;
break;
case 5:
str_info << "\nNo further error reduction is possible.\n"
<< "Restart with increased initial scaling factor.";
warning = 5;
break;
case 6:
str_info << "\nStopped by small squared error.";
warning = 0;
break;
case 7:
str_info << "\nStopped by invalid (i.e. NaN or Inf) \"func\" values.\n";
str_info << "This is a user error.";
warning = 7;
break;
default:
str_info << "\nUnknown reason for stopping the fit.";
warning = -1;
}
if (use_scaling && !can_scale) {
str_info << "\nCouldn't use scaling because one or more "
<< "of the parameters don't allow it.";
}
info=str_info.str();
return info_id[1];
}
const float* data_ptr(const Ndarray* a, int x) {
return data_ptr((Ndarray*) a, x);
}
std::tuple<at::Tensor, at::Tensor> mkldnn_convolution_backward_weights(
IntList weight_size, const at::Tensor& grad_output, const at::Tensor& input,
IntList padding, IntList stride, IntList dilation, bool bias_defined)
{
auto grad_weight = grad_output.type().tensor(weight_size);
Tensor grad_bias;
if (bias_defined) {
grad_bias = grad_output.type().tensor({grad_output.size(1)});
}
auto cpu_engine = CpuEngine::Instance().get_engine();
int32_t n = input.size(0);
int32_t ic = input.size(1);
int32_t ih = input.size(2);
int32_t iw = input.size(3);
int32_t oc = grad_output.size(1);
int32_t oh = grad_output.size(2);
int32_t ow = grad_output.size(3);
int32_t kh = grad_weight.size(2);
int32_t kw = grad_weight.size(3);
int32_t sh = stride[0];
int32_t sw = stride[1];
int32_t ph = padding[0];
int32_t pw = padding[1];
auto data_t = memory::data_type::f32;
auto format_any = memory::format::any;
auto format_nchw = memory::format::nchw;
auto format_oihw = memory::format::oihw;
auto format_x = memory::format::x;
memory::dims input_tz = {n, ic, ih, iw};
memory::dims weight_tz = {oc, ic, kh, kw};
memory::dims bias_tz = {oc};
memory::dims output_tz = {n, oc, oh, ow};
memory::dims _stride = {sh, sw};
memory::dims _padding = {ph, pw};
memory::desc input_md({input_tz}, data_t, format_any);
memory::desc weight_md({weight_tz}, data_t, format_any);
memory::desc bias_md({bias_tz}, data_t, format_any);
memory::desc output_md({output_tz}, data_t, format_any);
// need to re-create conv_forward_pd to feed conv_backward_weight_pd
std::shared_ptr<convolution_forward::desc> conv_forward_desc;
if (bias_defined) {
conv_forward_desc.reset(new convolution_forward::desc(prop_kind::forward,
convolution_direct, input_md, weight_md, bias_md, output_md,
_stride, _padding, _padding, padding_kind::zero));
} else {
conv_forward_desc.reset(new convolution_forward::desc(prop_kind::forward,
convolution_direct, input_md, weight_md, output_md,
_stride, _padding, _padding, padding_kind::zero));
}
std::shared_ptr<convolution_forward::primitive_desc> conv_forward_pd;
conv_forward_pd.reset(new convolution_forward::primitive_desc(
*conv_forward_desc, cpu_engine));
std::shared_ptr<convolution_backward_weights::desc> conv_backward_weight_desc;
if (bias_defined) {
conv_backward_weight_desc.reset(new convolution_backward_weights::desc(
convolution_direct, input_md, weight_md, bias_md, output_md,
_stride, _padding, _padding, padding_kind::zero));
} else {
conv_backward_weight_desc.reset(new convolution_backward_weights::desc(
convolution_direct, input_md, weight_md, output_md,
_stride, _padding, _padding, padding_kind::zero));
}
std::shared_ptr<convolution_backward_weights::primitive_desc> conv_backward_weight_pd;
conv_backward_weight_pd.reset(new convolution_backward_weights::primitive_desc(
*conv_backward_weight_desc, cpu_engine, *conv_forward_pd));
auto input_usr_memory = memory({{{input_tz}, data_t, format_nchw}, cpu_engine},
input.data_ptr());
auto grad_output_usr_memory = memory({{{output_tz}, data_t, format_nchw}, cpu_engine},
grad_output.data_ptr());
auto grad_weight_usr_memory = memory({{{weight_tz}, data_t, format_oihw}, cpu_engine},
grad_weight.data_ptr());
std::shared_ptr<memory> grad_bias_memory;
std::vector<primitive> net;
auto input_pd = conv_backward_weight_pd->src_primitive_desc();
auto input_memory = input_usr_memory;
if (input_usr_memory.get_primitive_desc() != memory::primitive_desc(input_pd)) {
input_memory = memory(input_pd);
net.push_back(reorder(input_usr_memory, input_memory));
}
auto grad_output_pd = conv_backward_weight_pd->diff_dst_primitive_desc();
auto grad_output_memory = grad_output_usr_memory;
if (grad_output_usr_memory.get_primitive_desc() != memory::primitive_desc(grad_output_pd)) {
grad_output_memory = memory(grad_output_pd);
net.push_back(reorder(grad_output_usr_memory, grad_output_memory));
}
auto grad_weight_pd = conv_backward_weight_pd->diff_weights_primitive_desc();
auto grad_weight_memory = grad_weight_usr_memory;
if (grad_weight_usr_memory.get_primitive_desc() != memory::primitive_desc(grad_weight_pd)) {
grad_weight_memory = memory(grad_weight_pd);
}
std::shared_ptr<convolution_backward_weights> conv_backward_weight;
if (bias_defined) {
grad_bias_memory.reset(new memory({{{bias_tz}, data_t, format_x}, cpu_engine},
grad_bias.data_ptr()));
conv_backward_weight.reset(new convolution_backward_weights(*conv_backward_weight_pd,
input_memory, grad_output_memory, grad_weight_memory, *grad_bias_memory));
} else {
conv_backward_weight.reset(new convolution_backward_weights(*conv_backward_weight_pd,
input_memory, grad_output_memory, grad_weight_memory));
}
net.push_back(*conv_backward_weight);
if (grad_weight_memory != grad_weight_usr_memory) {
net.push_back(reorder(grad_weight_memory, grad_weight_usr_memory));
}
Stream::Instance().get_stream().submit(net);
return std::tuple<at::Tensor, at::Tensor>{grad_weight, grad_bias};
}
