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Commit 6d067132 authored by Scott Cyphers's avatar Scott Cyphers
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Merge branch 'cyphers/dochow' of… 

Merge branch 'cyphers/dochow' of https://github.com/NervanaSystems/private-ngraph-cpp into cyphers/dochow
parents fe413386 3d615723
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doc/sphinx/ngraph_theme/static/css/theme.css
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......@@ -1837,18 +1837,19 @@ div[class^='highlight'] td.code {
}
code, p.caption, caption-text {
font-family: RobotoSlab, sans, monospace;
font-family: Inconsolata, sans, monospace;
color: #A79992;
font-size: 0.95em;
line-height: 1.11em;
font-size: 0.99em;
line-height: 1.39em;
}
.code-block-caption {
font-variant: small-caps;
font-size: 0.88em;
background-color: #c3d5d5;
background-color: #d0dfdf;
padding-right: 0.43em;
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padding-left: 0.11em;
padding-bottom: 0.23em;
text-align: right;
}
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doc/sphinx/source/howto/execute.rst
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......@@ -7,12 +7,13 @@ Executing a Computation
This section explains how to manually perform the steps that would normally be
performed by a framework :term:`bridge` to execute a computation. Intel® nGraph
library is targeted toward automatic construction; it is far easier for a
processing unit (GPU, CPU, or custom silicon) to run a computation than it is
for a user to map out how that computation happens.
processing unit (GPU, CPU, or NNP) to run a computation than it is for a user
to map out how that computation happens. Unfortunately, things that make by-hand
graph construction simpler tend to make automatic construction more difficult,
and vice versa.
Here we will do all the bridge steps manually. Unfortunately, things that make
by-hand graph construction simpler tend to make automatic construction more
difficult, and vice versa.
Here we will do all the bridge steps manually. The model description we'll write
is based on the :file:`abc.cpp` file in the ``/doc/examples/`` directory.
In order to successfully run a computation, the entity (framework or user) must
be able to do all of these things:
......@@ -37,13 +38,14 @@ from a framework's representation of the computation, graph construction can be
somewhat more tedious for users. To a user, who is usually interested in
specific nodes (vertices) or edges of a computation that reveal "what is
happening where", it can be helpful to think of a computation as a zoomed-out
and *stateless* dataflow graph where all of the nodes are basic well-defined
tensor operations and all of the edges denote use of an output from
one operation as an input for another operation.
and *stateless* dataflow graph where all of the nodes are well-defined tensor
operations and all of the edges denote use of an output from one operation as
an input for another operation.
.. TODO
.. image for representing nodes and edges
.. image for representing nodes and edges of (a+b)*c
Most of the public portion of the nGraph API is in the ``ngraph`` namespace, so
we will omit the namespace. Use of namespaces other than ``std`` will be
......@@ -63,47 +65,40 @@ pointers is given in the glossary.
Every node has zero or more *inputs*, zero or more *outputs*, and zero or more
*attributes*. For our purpose to :ref:`define_cmp`, nodes should be thought of
as essentially immutable; that is, when constructing a node, we need to supply
all of its inputs. We get this process started with ops that have no
inputs, since any op with inputs is going to first need some inputs.
all of its inputs. We get this process started with ops that have no inputs,
since any op with no inputs is going to first need some inputs.
``op::Parameter`` specifes the tensors that will be passed to the computation.
They receive their values from outside of the graph, so they have no inputs.
They have attributes for the element type and the shape of the tensor that will
be passed to them.
.. code-block:: cpp
Shape s{2, 3};
auto a = std::make_shared<op::Parameter>(element::f32, s);
auto b = std::make_shared<op::Parameter>(element::f32, s);
auto c = std::make_shared<op::Parameter>(element::f32, s);
Here we have made three parameter nodes, each a 32-bit float of shape
``(2, 3)`` using a row-major element layout.
.. literalinclude:: ../../../examples/abc.cpp
:language: cpp
:lines: 11-13
We can create a graph for ``(a+b)*c`` by creating an ``op::Add`` node
with inputs from ``a`` and ``b``, and an ``op::Multiply`` node from
the add node and ``c``:
Here we have made three parameter nodes, each a 32-bit float of shape ``(2, 3)``
using a row-major element layout.
.. code-block:: cpp
We can create a graph for ``(a+b)*c`` by creating an ``op::Add`` node with inputs
from ``a`` and ``b``, and an ``op::Multiply`` node from the add node and ``c``:
auto t0 = std::make_shared<op::Add>(a, b);
auto t1 = std::make_shared<op::Multiply(t0, c);
.. literalinclude:: ../../../examples/abc.cpp
:language: cpp
:lines: 15-16
When the ``op::Add`` op is constructed, it will check that the element
types and shapes of its inputs match; to support multiple frameworks,
ngraph does not do automatic type conversion or broadcasting. In this
case, they match, and the shape of the unique output of ``t0`` will be
a 32-bit float with shape ``(2, 3)``. Similarly, ``op::Multiply``
checks that its inputs match and sets the element type and shape of
its unique output.
When the ``op::Add`` op is constructed, it will check that the element types and
shapes of its inputs match; to support multiple frameworks, ngraph does not do
automatic type conversion or broadcasting. In this case, they match, and the
shape of the unique output of ``t0`` will be a 32-bit float with shape ``(2, 3)``.
Similarly, ``op::Multiply``checks that its inputs match and sets the element
type and shape of its unique output.
Once the graph is built, we need to package it in a ``Function``:
.. code-block:: cpp
auto f = make_shared<Function>(NodeVector{t1}, ParameterVector{a, b, c});
.. literalinclude:: ../../../examples/abc.cpp
:language: cpp
:lines: 19
The first argument to the constuctor specifies the nodes that the function will
return; in this case, the product. A ``NodeVector`` is a vector of shared
......@@ -111,7 +106,7 @@ pointers of ``op::Node``. The second argument specifies the parameters of the
function, in the order they are to be passed to the compiled function. A
``ParameterVector`` is a vector of shared pointers to ``op::Parameter``.
.. important:: The parameter vector must include* **every** parameter used in
.. important:: The parameter vector must include **every** parameter used in
the computation of the results.
......@@ -121,43 +116,42 @@ Specify the backend upon which to run the computation
=====================================================
For a framework bridge, a *backend* is the environment that can perform the
computations; it can be done with a CPU, GPU, or an NNP. A *transformer* can
compile computations for a backend, allocate and deallocate tensors, and invoke
computations.
computations; it can be done with a CPU, GPU, or an Intel Nervana NNP. A
*transformer* can compile computations for a backend, allocate and deallocate
tensors, and invoke computations.
Factory-like managers for classes of backend managers can compile a ``Function``
and allocate backends. A backend is somewhat analogous to a multi-threaded
process.
There are two backends for the CPU, the optimized "CPU" backend, which
makes use of mkl-dnn, and the "INTERPRETER" backend which runs
reference versions of kernels where implementation clarity is favored
over speed. The "INTERPRETER" backend is mainly used for testing.
There are two backends for the CPU: the optimized ``"CPU"`` backend, which uses
the `Intel MKL-DNN`_, and the ``"INTERPRETER"`` backend, which runs reference
versions of kernels that favor implementation clarity over speed. The
``"INTERPRETER"`` backend can be slow, and is primarily intended for testing.
To select the "CPU" backend,
To select the ``"CPU"`` backend,
.. code-block:: cpp
.. literalinclude:: ../../../examples/abc.cpp
:language: cpp
:lines: 22-23
auto manager = runtime::Manager::get("CPU");
auto backend = manager->allocate_backend();
.. _compile_cmp:
Compile the computation
=======================
Compilation produces something misnamed an ``ExternalFunction``, which
is a factory for producing a ``CallFrame``, a function and associated
state that can run in a single thread at a time. A ``CallFrame`` may
be reused, but any particular ``CallFrame`` must only be running in
one thread at any time. If more than one thread needs to execute the
function at the same time, create multiple ``CallFrame`` objects from
the ``ExternalFunction``.
Compilation triggers something that can be used as a factory for producing a
``CallFrame`` which is a *function* and its associated *state* that can run
in a single thread at a time. A ``CallFrame`` may be reused, but any particular
``CallFrame`` must only be running in one thread at any time. If more than one
thread needs to execute the function at the same time, create multiple
``CallFrame`` objects from the ``ExternalFunction``.
.. code-block:: cpp
.. literalinclude:: ../../../examples/abc.cpp
:language: cpp
:lines: 24-28
auto external = manager->compile(f);
auto cf = backend->make_call_frame(external);
.. _allocate_bkd_storage:
......@@ -178,12 +172,9 @@ Backends are responsible for managing storage. If the storage is off-CPU, caches
are used to minimize copying between device and CPU. We can allocate storage for
the three parameters and return value as follows:
.. code-block:: cpp
auto t_a = backend->make_primary_tensor_view(element::f32, shape);
auto t_b = backend->make_primary_tensor_view(element::f32, shape);
auto t_c = backend->make_primary_tensor_view(element::f32, shape);
auto t_result = backend->make_primary_tensor_view(element::f32, shape);
.. literalinclude:: ../../../examples/abc.cpp
:language: cpp
:lines: 30-33
Each tensor is a shared pointer to a ``runtime::TensorView``, the interface
backends implement for tensor use. When there are no more references to the
......@@ -194,18 +185,17 @@ tensor view, it will be freed when convenient for the backend.
Initialize the inputs
=====================
Normally the framework bridge reads/writes bytes to the tensor, assuming a
Normally the framework bridge reads and writes bytes to the tensor, assuming a
row-major element layout. To simplify writing unit tests, we have developed a
class for making tensor literals. We can use these to initialize our tensors:
.. code-block:: cpp
.. literalinclude:: ../../../examples/abc.cpp
:language: cpp
:lines: 36-38
copy_data(t_a, test::NDArray<float, 2>({{1, 2, 3}, {4, 5, 6}}).get_vector());
copy_data(t_b, test::NDArray<float, 2>({{7, 8, 9}, {10, 11, 12}}).get_vector());
copy_data(t_c, test::NDArray<float, 2>({{1, 0, -1}, {-1, 1, 2}}).get_vector());
The ``test::NDArray`` needs to know the element type (``float``) and rank (``2``)
of the tensors, and figures out the shape during template expansion.
The ``test::NDArray`` needs to know the element type (``float`` for this
example) and rank (``2`` for this example) of the tensors; it will then
populate the shape during template expansion.
The ``runtime::TensorView`` interface has ``write`` and ``read`` methods for
copying data to/from the tensor.
......@@ -215,12 +205,13 @@ copying data to/from the tensor.
Invoke the computation
======================
To invoke the function, we simply pass argument and result tensors to
the call frame:
To invoke the function, we simply pass argument and resultant tensors to the
call frame:
.. code-block:: cpp
.. literalinclude:: ../../../examples/abc.cpp
:language: cpp
:lines: 41
cf->call({t_a, t_b, t_c}, {t_result});
.. _access_outputs:
......@@ -229,61 +220,17 @@ Access the outputs
We can use the ``read`` method to access the result:
.. code-block:: cpp
float r[2,3];
t_result->read(&r, 0, sizeof(r));
.. literalinclude:: ../../../examples/abc.cpp
:language: cpp
:lines: 44-45
.. _all_together:
Putting it all together
=======================
.. code-block:: cpp
#include <iostream>
#include <ngraph.hpp>
using namespace ngraph;
void main()
{
// Build the graph
Shape s{2, 3};
auto a = std::make_shared<op::Parameter>(element::f32, s);
auto b = std::make_shared<op::Parameter>(element::f32, s);
auto c = std::make_shared<op::Parameter>(element::f32, s);
auto t0 = std::make_shared<op::Add>(a, b);
auto t1 = std::make_shared < op::Multiply(t0, c);
// Make the function
auto f = make_shared<Function>(NodeVector{t1}, ParameterVector{a, b, c});
// Get the backend
auto manager = runtime::Manager::get("CPU");
auto backend = manager->allocate_backend();
auto external = manager->compile(f);
// Compile the function
auto cf = backend->make_call_frame(external);
// Allocate tensors
auto t_a = backend->make_primary_tensor_view(element::f32, shape);
auto t_b = backend->make_primary_tensor_view(element::f32, shape);
auto t_c = backend->make_primary_tensor_view(element::f32, shape);
auto t_result = backend->make_primary_tensor_view(element::f32, shape);
// Initialize tensors
copy_data(t_a, test::NDArray<float, 2>({{1, 2, 3}, {4, 5, 6}}).get_vector());
copy_data(t_b, test::NDArray<float, 2>({{7, 8, 9}, {10, 11, 12}}).get_vector());
copy_data(t_c, test::NDArray<float, 2>({{1, 0, -1}, {-1, 1, 2}}).get_vector());
// Invoke the function
cf->call({t_a, t_b, t_c}, {t_result});
.. literalinclude:: ../../../examples/abc.cpp
:language: cpp
:lines: 1-46
:caption: "The (a + b) * c example for executing a computation on nGraph"
// Get the result
float r[2, 3];
t_result->read(&r, 0, sizeof(r));
}
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