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Commit 99e948db authored by Davis King's avatar Davis King
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Added tensor spec.

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dlib/dnn/gpu_data_abstract.h
View file @ 99e948db
...@@ -16,23 +16,23 @@ namespace dlib ...@@ -16,23 +16,23 @@ namespace dlib
/*! /*!
WHAT THIS OBJECT REPRESENTS WHAT THIS OBJECT REPRESENTS
This object is a block of size() floats, all stored contiguously in memory. This object is a block of size() floats, all stored contiguously in memory.
In particular, it keeps two copies of the floats, one on the host CPU side Importantly, it keeps two copies of the floats, one on the host CPU side
and another on the GPU device side. It automatically performs the necessary and another on the GPU device side. It automatically performs the necessary
host/device transfers to keep these two copies of the data in sync. host/device transfers to keep these two copies of the data in sync.
All transfers to the device happen asynchronously so that CUDA kernel All transfers to the device happen asynchronously with respect to the
computations can overlap with data transfers. However, any transfers from default CUDA stream so that CUDA kernel computations can overlap with data
the device to the host happen synchronously in the default CUDA stream. transfers. However, any transfers from the device to the host happen
Therefore, you should perform all your CUDA kernel launches on the default synchronously in the default CUDA stream. Therefore, you should perform
stream so that transfers back to the host do not happen before the all your CUDA kernel launches on the default stream so that transfers back
computations have completed. to the host do not happen before the relevant computations have completed.
If DLIB_USE_CUDA is not #defined then this object will not use CUDA at all. If DLIB_USE_CUDA is not #defined then this object will not use CUDA at all.
Instead, it will simply store one host side memory block of floats. Instead, it will simply store one host side memory block of floats.
THREAD SAFETY THREAD SAFETY
This object is not thread-safe. Don't touch it from multiple threads at Instances of this object are not thread-safe. So don't touch one from
the same time. multiple threads at the same time.
!*/ !*/
public: public:
......
dlib/dnn/tensor.h
View file @ 99e948db
...@@ -367,6 +367,7 @@ namespace dlib ...@@ -367,6 +367,7 @@ namespace dlib
const tensor& b const tensor& b
) )
{ {
// TODO, do on GPU?
DLIB_CASSERT(a.size() == b.size(), ""); DLIB_CASSERT(a.size() == b.size(), "");
const float* da = a.host(); const float* da = a.host();
const float* db = b.host(); const float* db = b.host();
......
dlib/dnn/tensor_abstract.h 0 → 100644
View file @ 99e948db
// Copyright (C) 2015 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_DNn_TENSOR_ABSTRACT_H_
#ifdef DLIB_DNn_TENSOR_ABSTRACT_H_
#include <cstring>
#include "../matrix.h"
#include "cudnn_dlibapi.h"
#include "gpu_data.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
class tensor
{
/*!
WHAT THIS OBJECT REPRESENTS
This object represents a 4D array of float values, all stored contiguously
in memory. Importantly, it keeps two copies of the floats, one on the host
CPU side and another on the GPU device side. It automatically performs the
necessary host/device transfers to keep these two copies of the data in
sync.
All transfers to the device happen asynchronously with respect to the
default CUDA stream so that CUDA kernel computations can overlap with data
transfers. However, any transfers from the device to the host happen
synchronously in the default CUDA stream. Therefore, you should perform
all your CUDA kernel launches on the default stream so that transfers back
to the host do not happen before the relevant computations have completed.
If DLIB_USE_CUDA is not #defined then this object will not use CUDA at all.
Instead, it will simply store one host side memory block of floats.
Finally, the convention in dlib code is to interpret the tensor as a set of
num_samples() 3D arrays, each of dimension k() by nr() by nc(). Also,
while this class does not specify a memory layout, the convention is to
assume that indexing into an element at coordinates (sample,k,nr,nc) can be
accomplished via:
host()[((sample*t.k() + k)*t.nr() + nr)*t.nc() + nc]
THREAD SAFETY
Instances of this object are not thread-safe. So don't touch one from
multiple threads at the same time.
!*/
public:
// A tensor is an abstract type. Therefore, you use the resizable_tensor object
// below to create tensor instances.
virtual ~tensor() = 0;
long num_samples(
) const;
/*!
ensures
- returns the number of 3D arrays of dimension k() by nr() by nc() there
are in this object.
!*/
long k(
) const;
/*!
ensures
- returns the k dimension of this tensor. Generally, we think of a tensor
as containing num_samples() images of nr() by nc() rows and columns, each
with k() channels.
!*/
long nr(
) const;
/*!
ensures
- returns the number of rows in this tensor.
!*/
long nc(
) const;
/*!
ensures
- returns the number of columns in this tensor.
!*/
size_t size(
) const;
/*!
ensures
- returns num_samples()*k()*nr()*nc()
(i.e. the total number of floats in this tensor)
!*/
void async_copy_to_device(
) const;
/*!
ensures
- This function does not block.
- if (the host version of the data is newer than the device's copy) then
- Begins asynchronously copying host data to the device.
- A call to device() that happens before the transfer completes will
block until the transfer is complete. That is, it is safe to call
async_copy_to_device() and then immediately call device().
!*/
const float* host(
) const;
/*!
ensures
- returns a pointer to the host memory block of size() contiguous float
values or nullptr if size()==0.
- if (the host's copy of the data is out of date) then
- copies the data from the device to the host, while this is happening
the call to host() blocks.
!*/
float* host(
);
/*!
ensures
- returns a pointer to the host memory block of size() contiguous float
values or nullptr if size()==0.
- if (the host's copy of the data is out of date) then
- copies the data from the device to the host, while this is happening
the call to host() blocks.
- Marks the device side data as out of date so that the next call to
device() will perform a host to device transfer. If you want to begin
the transfer immediately then you can call async_copy_to_device() after
calling host().
!*/
const float* device(
) const;
/*!
requires
- DLIB_USE_CUDA is #defined
ensures
- returns a pointer to the device memory block of size() contiguous float
values or nullptr if size()==0.
- if (the device's copy of the data is out of date) then
- copies the data from the host to the device, while this is happening
the call to device() blocks.
!*/
float* device(
);
/*!
requires
- DLIB_USE_CUDA is #defined
ensures
- returns a pointer to the device memory block of size() contiguous float
values or nullptr if size()==0.
- if (the device's copy of the data is out of date) then
- copies the data from the host to the device, while this is happening
the call to device() blocks.
- Marks the host side data as out of date so that the next call to
host() will perform a device to host transfer.
!*/
tensor& operator= (
float val
);
/*!
ensures
- sets all elements of this tensor equal to val.
- returns *this
!*/
template <typename EXP>
tensor& operator= (
const matrix_exp<EXP>& item
);
/*!
requires
- num_samples() == item.nr()
- k()*nr()*nc() == item.nc()
- item contains float values
ensures
- Assigns item to *this tensor by performing:
set_ptrm(host(), num_samples(), k()*nr()*nc()) = item;
!*/
template <typename EXP>
tensor& operator+= (
const matrix_exp<EXP>& item
);
/*!
requires
- num_samples() == item.nr()
- k()*nr()*nc() == item.nc()
- item contains float values
ensures
- Adds item to *this tensor by performing:
set_ptrm(host(), num_samples(), k()*nr()*nc()) += item;
!*/
template <typename EXP>
tensor& operator-= (
const matrix_exp<EXP>& item
);
/*!
requires
- num_samples() == item.nr()
- k()*nr()*nc() == item.nc()
- item contains float values
ensures
- Subtracts item from *this tensor by performing:
set_ptrm(host(), num_samples(), k()*nr()*nc()) -= item;
!*/
template <typename EXP>
void set_sample (
unsigned long idx,
const matrix_exp<EXP>& item
);
/*!
requires
- idx < num_samples()
- k()*nr()*nc() == item.size()
- item contains float values
ensures
- Assigns item to the idx'th sample in *this by performing:
set_ptrm(host()+idx*item.size(), item.nr(), item.nc()) = item;
!*/
template <typename EXP>
void add_to_sample (
unsigned long idx,
const matrix_exp<EXP>& item
);
/*!
requires
- idx < num_samples()
- k()*nr()*nc() == item.size()
- item contains float values
ensures
- Adds item to the idx'th sample in *this by performing:
set_ptrm(host()+idx*item.size(), item.nr(), item.nc()) += item;
!*/
protected:
// You can't move or copy another tensor into *this since that might modify the
// tensor's dimensions. If you want to do that sort of thing then use a
// resizable_tensor.
tensor(const tensor& item);
tensor& operator= (const tensor& item);
tensor(tensor&& item);
tensor& operator=(tensor&& item);
};
// ----------------------------------------------------------------------------------------
const matrix_exp mat (
const tensor& t,
long nr,
long nc
);
/*!
requires
- nr > 0
- nc > 0
- nr*nc == t.size()
ensures
- returns a matrix M such that:
- M.nr() == nr
- m.nc() == nc
- for all valid r and c:
M(r,c) == t.host()[r*nc + c]
(i.e. the tensor is interpreted as a matrix laid out in memory
in row major order)
!*/
const matrix_exp mat (
const tensor& t
);
/*!
requires
- t.size() != 0
ensures
- returns mat(t, t.num_samples(), t.size()/t.num_samples())
!*/
// ----------------------------------------------------------------------------------------
bool have_same_dimensions (
const tensor& a,
const tensor& b
);
/*!
ensures
- returns true if and only if all of the fallowing are satisfied:
- a.num_samples() == b.num_samples()
- a.k() == b.k()
- a.nr() == b.nr()
- a.nc() == b.nc()
!*/
// ----------------------------------------------------------------------------------------
class resizable_tensor : public tensor
{
/*!
WHAT THIS OBJECT REPRESENTS
This object is just a tensor with the additional ability to be resized.
!*/
public:
resizable_tensor(
);
/*!
ensures
- #size() == 0
- #num_samples() == 0
- #k() == 0
- #nr() == 0
- #nc() == 0
!*/
explicit resizable_tensor(
long n_, long k_ = 1, long nr_ = 1, long nc_ = 1
);
/*!
requires
- n_ >= 0
- k_ >= 0
- nr_ >= 0
- nc_ >= 0
ensures
- #size() == n_*k_*nr_*nc_
- #num_samples() == n_
- #k() == k_
- #nr() == nr_
- #nc() == nc_
!*/
// This object is copyable and movable
resizable_tensor(const resizable_tensor&) = default;
resizable_tensor(resizable_tensor&&) = default;
resizable_tensor& operator= (const resizable_tensor&) = default;
resizable_tensor& operator= (resizable_tensor&&) = default;
void clear(
);
/*!
ensures
- #size() == 0
- #num_samples() == 0
- #k() == 0
- #nr() == 0
- #nc() == 0
!*/
void copy_size (
const tensor& item
);
/*!
ensures
- resizes *this so that: have_same_dimensions(#*this, item)==true
!*/
void set_size(
long n_, long k_ = 1, long nr_ = 1, long nc_ = 1
);
/*!
requires
- n_ >= 0
- k_ >= 0
- nr_ >= 0
- nc_ >= 0
ensures
- #size() == n_*k_*nr_*nc_
- #num_samples() == n_
- #k() == k_
- #nr() == nr_
- #nc() == nc_
!*/
};
void serialize(const tensor& item, std::ostream& out)
void deserialize(resizable_tensor& item, std::istream& in);
/*!
provides serialization support for tensor and resizable_tensor. Note that you can
serialize to/from any combination of tenor and resizable_tensor objects.
!*/
// ----------------------------------------------------------------------------------------
double dot(
const tensor& a,
const tensor& b
);
/*!
requires
- a.size() == b.size()
ensures
- returns the dot product between a and b when they are both treated as
a.size() dimensional vectors. That is, this function pointwise multiplies
the vectors together, then sums the result and returns it.
!*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_DNn_TENSOR_ABSTRACT_H_
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