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Commit b8234498 by Tianqi Chen Committed by GitHub
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[DOC] API doc organization. (#90) 

* [DOC] API doc organization.

* remove breathe for now
parent b5702eca
Showing with 606 additions and 226 deletions
docs/_static/css/tvm_theme.css 0 → 100644
.rst-content .hidden-section {
display: none;
}
.rst-toc .hidden-section {
display: none;
}
nav .hidden-section {
display: inherit;
}
docs/api/python/build.rst 0 → 100644
tvm.build
---------
.. autofunction:: tvm.lower
.. autofunction:: tvm.build
docs/api/python/collection.rst 0 → 100644
tvm.collections
---------------
Collections contains data structures used in TVM DSL.
.. automodule:: tvm.collections
:members:
docs/api/python/dev.rst 0 → 100644
Developer API
-------------
This page contains modules that are used by developers of TVM.
tvm.node
~~~~~~~~
Node is the base class of all TVM AST. Normally user do not
need to touch this api.
.. autoclass:: tvm.node.NodeBase
:members:
.. autoclass:: tvm.node.Node
:members:
.. autofunction:: tvm.register_node
tvm.expr
~~~~~~~~
.. automodule:: tvm.expr
:members:
:undoc-members:
tvm.codegen
~~~~~~~~~~~
.. automodule:: tvm.codegen
:members:
:undoc-members:
tvm.stmt
~~~~~~~~
.. automodule:: tvm.stmt
:members:
:undoc-members:
tvm.ir_pass
~~~~~~~~~~~
.. automodule:: tvm.ir_pass
:members:
.. autosummary::
tvm.ir_pass.Inline
tvm.ir_pass.Simplify
tvm.ir_pass.ConvertSSA
tvm.ir_pass.VerifySSA
tvm.ir_pass.CanonicalSimplify
tvm.ir_pass.StorageFlatten
tvm.ir_pass.VectorizeLoop
tvm.ir_pass.UnrollLoop
tvm.ir_pass.StorageSync
tvm.ir_pass.MakeAPI
tvm.ir_pass.SplitHostDevice
tvm.ir_pass.InjectVirtualThread
tvm.ir_pass.LoopPartition
tvm.ir_pass.RemoveNoOp
tvm.ir_pass.SplitPipeline
tvm.ir_pass.LowerThreadAllreduce
tvm.ir_pass.NarrowChannelAccess
tvm.make
~~~~~~~~
.. automodule:: tvm.make
:members:
docs/api/python/function.rst 0 → 100644
tvm.Function
------------
.. autoclass:: tvm.Function
.. autofunction:: tvm.register_func
.. autofunction:: tvm.get_global_func
docs/api/python/index.rst 0 → 100644
Python API
==========
.. toctree::
:maxdepth: 2
tvm
tensor
schedule
build
module
ndarray
collection
node
function
dev
docs/api/python/module.rst 0 → 100644
tvm.module
----------
.. autoclass:: tvm.module.Module
:members:
:inherited-members:
.. autofunction:: tvm.module.load
.. autofunction:: tvm.module.enabled
docs/api/python/ndarray.rst 0 → 100644
tvm.ndarray
-----------
tvm.ndarray provides a minimum runtime array API to testing out
the correctness of the program.
.. autoclass:: tvm.ndarray.TVMContext
:members:
.. autoclass:: tvm.ndarray.NDArray
:members:
:inherited-members:
.. autofunction:: tvm.cpu
.. autofunction:: tvm.gpu
.. autofunction:: tvm.opencl
.. autofunction:: tvm.ndarray.array
docs/api/python/schedule.rst 0 → 100644
tvm.schedule
------------
The `tvm.schedule` module contains classes of scheduling
structure of tvm.
.. autoclass:: tvm.schedule.IterVar
:members:
.. autofunction:: tvm.create_schedule
.. autoclass:: tvm.schedule.Schedule
:members:
:inherited-members:
.. autoclass:: tvm.schedule.Stage
:members:
:inherited-members:
docs/api/python/tensor.rst 0 → 100644
tvm.tensor
----------
The `tvm.tensor` module contains declaration of Tensor
and Operation class for computation declaration.
.. autoclass:: tvm.tensor.Tensor
:members:
:inherited-members:
.. autoclass:: tvm.tensor.Operation
:members:
:inherited-members:
.. autoclass:: tvm.tensor.ComputeOp
:members:
:show-inheritance:
.. autoclass:: tvm.tensor.PlaceholderOp
:members:
:show-inheritance:
.. autoclass:: tvm.tensor.ScanOp
:members:
:show-inheritance:
.. autoclass:: tvm.tensor.ExternOp
:members:
:show-inheritance:
docs/api/python/tvm.rst 0 → 100644
tvm
---
tvm is a library root namespace contains functions for
declaring computation.
.. autosummary::
tvm.load_json
tvm.save_json
tvm.var
tvm.const
tvm.convert
tvm.placeholder
tvm.compute
tvm.scan
tvm.extern
tvm.call_packed
tvm.decl_buffer
tvm.reduce_axis
tvm.thread_axis
tvm.sum
tvm.min
tvm.max
.. autofunction:: tvm.load_json
.. autofunction:: tvm.save_json
.. autofunction:: tvm.var
.. autofunction:: tvm.const
.. autofunction:: tvm.convert
.. autofunction:: tvm.placeholder
.. autofunction:: tvm.compute
.. autofunction:: tvm.scan
.. autofunction:: tvm.extern
.. autofunction:: tvm.call_packed
.. autofunction:: tvm.decl_buffer
.. autofunction:: tvm.reduce_axis
.. autofunction:: tvm.thread_axis
.. autofunction:: tvm.sum
.. autofunction:: tvm.min
.. autofunction:: tvm.max
docs/conf.py
......@@ -26,7 +26,6 @@ curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
libpath = os.path.join(curr_path, '../python/')
sys.path.insert(0, libpath)
# -- General configuration ------------------------------------------------
# General information about the project.
......@@ -51,11 +50,15 @@ release = tvm.__version__
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.intersphinx',
'sphinx.ext.napoleon',
'sphinx.ext.mathjax',
'sphinx_gallery.gen_gallery',
]
breathe_projects = {'tvm' : 'doxygen/xml/'}
breathe_default_project = 'tvm'
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
......@@ -132,7 +135,7 @@ if not on_rtd and html_theme == 'rtd':
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
#html_static_path = ['_static']
html_static_path = ['_static']
# Output file base name for HTML help builder.
htmlhelp_basename = project + 'doc'
......@@ -153,12 +156,19 @@ latex_documents = [
def run_doxygen(folder):
"""Run the doxygen make command in the designated folder."""
try:
retcode = subprocess.call("cd %s; make doxygen" % folder, shell=True)
retcode = subprocess.call("cd %s; make doc" % folder, shell=True)
if retcode < 0:
sys.stderr.write("doxygen terminated by signal %s" % (-retcode))
except OSError as e:
sys.stderr.write("doxygen execution failed: %s" % e)
intersphinx_mapping = {
'python': ('https://docs.python.org/{.major}'.format(sys.version_info), None),
'numpy': ('http://docs.scipy.org/doc/numpy/', None),
'scipy': ('http://docs.scipy.org/doc/scipy/reference', None),
'matplotlib': ('http://matplotlib.org/', None),
}
examples_dirs = ['../tutorials/python']
gallery_dirs = ['tutorials']
......@@ -171,18 +181,24 @@ def generate_doxygen_xml(app):
def setup(app):
# Add hook for building doxygen xml when needed
# no c++ API for now
# app.connect("builder-inited", generate_doxygen_xml)
app.connect("builder-inited", generate_doxygen_xml)
app.add_stylesheet('css/tvm_theme.css')
app.add_config_value('recommonmark_config', {
'url_resolver': lambda url: github_doc_root + url,
}, True)
app.add_transform(AutoStructify)
sphinx_gallery_conf = {
'backreferences_dir': 'gen_modules/backreferences',
'doc_module': ('tvm'),
'reference_url': {
'tvm': None
},
'doc_module': ('tvm', 'numpy'),
'reference_url': {
'tvm': None,
'matplotlib': 'http://matplotlib.org',
'numpy': 'http://docs.scipy.org/doc/numpy-1.9.1'},
'examples_dirs': examples_dirs,
'gallery_dirs': gallery_dirs
'gallery_dirs': gallery_dirs,
'find_mayavi_figures': False,
'filename_pattern': '.py',
'expected_failing_examples': []
}
docs/index.rst
......@@ -9,7 +9,7 @@ Contents
.. toctree::
:maxdepth: 1
how_to/contribute
how_to/install
tutorials/index
python/api
api/python/index
how_to/contribute
docs/python/api.rst deleted 100644 → 0
Python API
==========
tvm
---
tvm is a library root namespace contains functions for
declaring computation.
.. autofunction:: tvm.load_json
.. autofunction:: tvm.save_json
.. autofunction:: tvm.var
.. autofunction:: tvm.convert
.. autofunction:: tvm.placeholder
.. autofunction:: tvm.compute
.. autofunction:: tvm.scan
.. autofunction:: tvm.extern
.. autofunction:: tvm.reduce_axis
.. autofunction:: tvm.sum
tvm.tensor
----------
The `tvm.tensor` module contains declaration of Tensor
and Operation class for computation declaration.
.. autoclass:: tvm.tensor.Tensor
:members:
:inherited-members:
.. autoclass:: tvm.tensor.Operation
:members:
:inherited-members:
tvm.schedule
------------
.. autofunction:: tvm.create_schedule
.. autoclass:: tvm.schedule.Schedule
:members:
.. autoclass:: tvm.schedule.Stage
:members:
tvm.build
---------
.. autofunction:: tvm.lower
.. autofunction:: tvm.build
tvm.ndarray
-----------
tvm.ndarray provides a minimum runtime array API to testing out
the correctness of the program.
.. autofunction:: tvm.cpu
.. autofunction:: tvm.gpu
.. autofunction:: tvm.vpi
.. autofunction:: tvm.opencl
.. autofunction:: tvm.ndarray.array
.. autoclass:: tvm.ndarray.TVMContext
:members:
.. autoclass:: tvm.ndarray.NDArray
:members:
:inherited-members:
tvm.Function
------------
.. autofunction:: tvm.register_func
.. autoclass:: tvm.Function
tvm.module
----------
.. autofunction:: tvm.module.load
.. autofunction:: tvm.module.load
.. autoclass:: tvm.module.Module
:members:
:inherited-members:
tvm.node
--------
tvm.node provides
.. autofunction:: tvm.register_node
.. autoclass:: tvm.node.NodeBase
:members:
.. autoclass:: tvm.node.Node
:members:
tvm.expr
--------
.. automodule:: tvm.expr
:members:
python/tvm/_ctypes/_function.py
......@@ -136,7 +136,27 @@ def _make_tvm_args(args, temp_args):
class Function(object):
"""The Function object used in TVM.
"""The PackedFunc object used in TVM.
Function plays an key role to bridge front and backend in TVM.
Function provide a type-erased interface, you can call function with positional arguments.
The compiled module returns Function.
TVM backend also registers and exposes its API as Functions.
For example, the developer function exposed in tvm.ir_pass are actually
C++ functions that are registered as PackedFunc
The following are list of common usage scenario of tvm.Function.
- Automatic exposure of C++ API into python
- To call PackedFunc from python side
- To call python callbacks to inspect results in generated code
- Bring python hook into C++ backend
See Also
--------
tvm.register_func: How to register global function.
tvm.get_global_func: How to get global function.
"""
__slots__ = ["handle", "is_global"]
# pylint: disable=no-member
......@@ -299,6 +319,26 @@ def register_func(func_name, f=None):
-------
fregister : function
Register function if f is not specified.
Examples
--------
The following code registers my_packed_func as global function.
Note that we simply get it back from global function table to invoke
it from python side. However, we can also invoke the same function
from C++ backend, or in the compiled TVM code.
.. code-block:: python
targs = (10, 10.0, "hello")
@tvm.register_func
def my_packed_func(*args):
assert(tuple(args) == targs)
return 10
# Get it out from global function table
f = tvm.get_global_func("my_packed_func")
assert isinstance(f, tvm.nd.Function)
y = f(*targs)
assert y == 10
"""
if callable(func_name):
f = func_name
......@@ -328,7 +368,7 @@ def get_global_func(name):
Returns
-------
func : tvm.nd.Function
func : tvm.Function
The function to be returned.
"""
handle = FunctionHandle()
......@@ -355,27 +395,60 @@ def list_global_func_names():
return fnames
def _init_api_functions(root_namespace):
"""List and add all the functions to current module."""
module_obj = sys.modules["%s.api" % root_namespace]
module_internal = sys.modules["%s._api_internal" % root_namespace]
def _get_api(f):
flocal = f
def my_api_func(*args):
"""
This is a type erased API that calls into Global PackedFunc.
These APIs corresponds to functions registered from C++ backend
and can be used as developer functions.
args : list
The positional arguments to the function call.
Returns
-------
value : int, float, None, Node or Function
The result of the API function call.
"""
return flocal(*args)
return my_api_func
def _init_api(mod):
"""Initialize api for a given module name
mod : str
The name of the module.
"""
module = sys.modules[mod]
namespace_match = {
"_make_": sys.modules["%s.make" % root_namespace],
"_arith_": sys.modules["%s.arith" % root_namespace],
"_pass_": sys.modules["%s.ir_pass" % root_namespace],
"_codegen_": sys.modules["%s.codegen" % root_namespace],
"_module_": sys.modules["%s.module" % root_namespace],
"_schedule_": sys.modules["%s.schedule" % root_namespace]
"_make_": "tvm.make",
"_arith_": "tvm.arith",
"_pass_": "tvm.ir_pass",
"_codegen_": "tvm.codegen",
"_module_": "tvm.module",
"_schedule_": "tvm.schedule"
}
for name in list_global_func_names():
fname = name
target_module = module_internal if name.startswith('_') else module_obj
target = "tvm.api"
for k, v in namespace_match.items():
if name.startswith(k):
fname = name[len(k):]
target_module = v
target = v
if target != mod:
continue
if mod == "tvm.api" and name.startswith("_"):
target_module = sys.modules["tvm._api_internal"]
else:
target_module = module
f = get_global_func(name)
setattr(target_module, fname, f)
ff = _get_api(f)
ff.__name__ = fname
ff.__doc__ = ("TVM PackedFunc %s. " % fname)
setattr(target_module, ff.__name__, ff)
def _init_module_module(module_class):
......
python/tvm/_ctypes/_node.py
......@@ -186,13 +186,14 @@ def register_node(type_key=None):
type_key : str or cls
The type key of the node
"""
node_name = type_key if isinstance(type_key, str) else type_key.__name__
def register(cls):
"""internal register function"""
NODE_TYPE[node_name] = cls
return cls
if isinstance(type_key, str):
def register(cls):
"""internal register function"""
NODE_TYPE[type_key] = cls
return cls
return register
else:
cls = type_key
NODE_TYPE[cls.__name__] = cls
return cls
return register(type_key)
python/tvm/api.py
......@@ -8,13 +8,14 @@ from ._ctypes._types import TVMType
from ._ctypes._node import register_node, NodeBase
from ._ctypes._node import convert_to_node as _convert_to_node
from ._ctypes._function import Function
from ._ctypes._function import _init_api_functions, register_func, get_global_func
from ._ctypes._function import _init_api, register_func, get_global_func
from ._ctypes._function import convert_to_tvm_func as _convert_tvm_func
from . import _api_internal
from . import _base
from . import make as _make
from . import expr as _expr
from . import tensor as _tensor
from . import schedule as _schedule
from . import collections as _collections
int32 = "int32"
......@@ -31,6 +32,27 @@ def const(value, dtype=None):
return _api_internal._const(value, dtype)
def convert(value):
"""Convert value to TVM node or function.
Parameters
----------
value : python value
Returns
-------
tvm_val : Node or Function
Converted value in TVM
"""
if isinstance(value, (Function, NodeBase)):
return value
if callable(value):
return _convert_tvm_func(value)
else:
return _convert_to_node(value)
def load_json(json_str):
"""Load tvm object from json_str.
......@@ -179,8 +201,8 @@ def scan(init, update, state_placeholder, inputs=None, name="scan"):
.. code-block:: python
# The following code is equivalent to numpy.cumsum
m = tvm.Var("m")
n = tvm.Var("n")
m = tvm.var("m")
n = tvm.var("n")
X = tvm.placeholder((m, n), name="X")
s_state = tvm.placeholder((m, n))
s_init = tvm.compute((1, n), lambda _, i: X[0, i])
......@@ -281,7 +303,10 @@ def decl_buffer(shape, dtype=None,
strides=None,
byte_offset=None,
offset_alignment=0):
"""Decleare a new symbolic buffer
"""Decleare a new symbolic buffer.
Normally buffer is created automatically during lower and build.
This is only needed if user want to specify their own buffer layout.
Parameters
----------
......@@ -370,6 +395,11 @@ def thread_axis(dom=None, tag='', name=''):
name : str, optional
The name of the var.
Returns
-------
axis : IterVar
The thread itervar.
"""
if isinstance(dom, _base.string_types):
tag, dom = dom, None
......@@ -446,7 +476,7 @@ def min(lhs, rhs=None, axis=None, where=None):
"""
if rhs and axis:
raise ValueError("Can only take one argument, rhs or axis")
if isinstance(rhs, (_collections.IterVar, list)):
if isinstance(rhs, (_schedule.IterVar, list)):
axis, rhs = rhs, axis
if rhs:
return _make.Min(lhs, rhs)
......@@ -479,7 +509,7 @@ def max(lhs, rhs=None, axis=None, where=None):
"""
if rhs and axis:
raise ValueError("Can only take one argument, rhs or axis")
if isinstance(rhs, (_collections.IterVar, list)):
if isinstance(rhs, (_schedule.IterVar, list)):
axis, rhs = rhs, axis
if rhs:
return _make.Max(lhs, rhs)
......@@ -487,26 +517,4 @@ def max(lhs, rhs=None, axis=None, where=None):
x = _make.Reduce("Max", expr, axis, where)
return x
def convert(value):
"""Convert value to TVM node or function.
Parameters
----------
value : python value
Returns
-------
tvm_val : Node or Function
Converted value in TVM
"""
if isinstance(value, (Function, NodeBase)):
return value
if callable(value):
return _convert_tvm_func(value)
else:
return _convert_to_node(value)
_init_api_functions("tvm")
_init_api("tvm.api")
python/tvm/arith.py
......@@ -2,6 +2,7 @@
from __future__ import absolute_import as _abs
from ._ctypes._node import NodeBase, register_node
from ._ctypes._function import _init_api
from . import _api_internal
class IntSet(NodeBase):
......@@ -36,3 +37,5 @@ class StrideSet(IntSet):
class ModularSet(IntSet):
"""Represent range of (coeff * x + base) for x in Z """
pass
_init_api("tvm.arith")
python/tvm/build.py
......@@ -134,10 +134,10 @@ def build(sch,
fapi = ir_pass.LowerThreadAllreduce(fapi, warp_size)
fsplits = [s for s in ir_pass.SplitHostDevice(fapi)]
if len(fsplits) > 1:
mhost = codegen.build(fsplits[0], target_host)
mhost = codegen.build_module(fsplits[0], target_host)
if target:
mdev = codegen.build(fsplits[1:], target)
mdev = codegen.build_module(fsplits[1:], target)
mhost.import_module(mdev)
return mhost
else:
return codegen.build(fsplits[0], target)
return codegen.build_module(fsplits[0], target)
python/tvm/codegen.py
"""Code generation related functions"""
"""Code generation related functions."""
from ._ctypes._function import _init_api
def build_module(lowered_func, target):
"""Build lowered_func into Module.
Parameters
----------
lowered_func : LoweredFunc
The lowered function
target : str
The target module type.
Returns
-------
module : Module
The corressponding module.
"""
return _Build(lowered_func, target)
def enabled(target):
"""Whether target is enabled for codegen.
Parameters
----------
target : str
The target module type.
Returns
-------
enabled : boolean
Whether the target module is enabled.
"""
return _Enabled(target)
_init_api("tvm.codegen")
python/tvm/collections.py
......@@ -2,11 +2,16 @@
from __future__ import absolute_import as _abs
from ._ctypes._node import NodeBase, register_node
from . import _api_internal
from . import expr as _expr
@register_node
class Array(NodeBase):
"""Array container of TVM"""
"""Array container of TVM.
You do not need to create Array explicitly.
Normally python list and tuple will be converted automatically
to Array during tvm function call.
You may get Array in return values of TVM function call.
"""
def __getitem__(self, i):
if isinstance(i, slice):
start = i.start if i.start is not None else 0
......@@ -26,7 +31,13 @@ class Array(NodeBase):
@register_node
class Map(NodeBase):
"""Map container of TVM"""
"""Map container of TVM.
You do not need to create Map explicitly.
Normally python dict will be converted automatically
to Array during tvm function call.
You may get Map in return values of TVM function call.
"""
def __getitem__(self, k):
return _api_internal._MapGetItem(self, k)
......@@ -47,22 +58,12 @@ class Map(NodeBase):
@register_node
class Range(NodeBase):
"""Represent range in TVM"""
pass
@register_node
class IterVar(NodeBase, _expr.ExprOp):
"""Represent iteration variable."""
DataPar = 0
ThreadIndex = 1
CommReduce = 2
Ordered = 3
DimInfo = 4
Unrolled = 5
Vectorized = 6
Parallelized = 7
"""Represent range in TVM.
You do not need to create Range explicitly.
Python list and tuple will be converted automatically to Range in api functions.
"""
pass
@register_node
class LoweredFunc(NodeBase):
......
python/tvm/expr.py
"""Module to declare Expression class"""
"""Expression AST Node in TVM.
User do not need to deal with expression AST node directly.
But they can be helpful for developer to do quick proptyping.
While not displayed in the document and python file.
Each expression node have subfields that can be visited from python side.
For example, you can use addexp.a to get the left operand of an Add node.
.. code-block:: python
x = tvm.var("n")
y = x + 2
assert(isinstance(y, tvm.expr.Add))
assert(y.a == x)
"""
# pylint: disable=missing-docstring
from __future__ import absolute_import as _abs
from ._ctypes._node import NodeBase, register_node
......@@ -75,7 +90,7 @@ class LogicalExpr(Expr):
@register_node("Variable")
class Var(Expr):
"""Symbolic variable expression."""
"""Symbolic variable."""
pass
@register_node
......
python/tvm/ir_pass.py
"""Namespace of IR pass functions"""
"""Namespace of IR pass functions.
This namespace is used for developers. While you do not see any declarations.
The functions are automatically exported from C++ side via PackedFunc.
Each api is a PackedFunc that can be called in a positional argument manner.
You can read "include/tvm/pass.h" for the function signature of these functions.
"""
from ._ctypes._function import _init_api
_init_api("tvm.ir_pass")
python/tvm/make.py
"""namespace of IR node builder make function"""
"""namespace of IR node builder make function
This namespace is used for developers. While you do not see any declarations.
The functions are automatically exported from C++ side via PackedFunc.
Each api is a PackedFunc that can be called in a positional argument manner.
You can use make function to build the IR node.
"""
from ._ctypes._function import _init_api
_init_api("tvm.make")
python/tvm/module.py
"""Runtime module related stuffs"""
from __future__ import absolute_import as _abs
from ._ctypes._function import ModuleBase, _init_module_module
from ._ctypes._function import _init_api
class Module(ModuleBase):
"""Module container of all TVM generated functions"""
......@@ -28,7 +30,7 @@ class Module(ModuleBase):
Returns
----------
modules : list of Modules
modules : list of Module
The module
"""
nmod = _ImportsSize(self)
......@@ -58,7 +60,36 @@ def load(path, fmt=""):
fmt : str, optional
The format of the file, if not specified
it will be inferred from suffix of the file.
Returns
-------
module : Module
The loaded module
"""
return _LoadFromFile(path, fmt)
def enabled(target):
"""Whether module runtime is enabled for target
Parameters
----------
target : str
The target device type.
Returns
-------
enabled : bool
Whether runtime is enabled.
Examples
--------
The following code checks if gpu is enabled.
>>> tvm.module.enabled("gpu")
"""
return _Enabled(target)
_init_api("tvm.module")
_init_module_module(Module)
python/tvm/schedule.py
......@@ -3,11 +3,14 @@ from __future__ import absolute_import as _abs
from ._ctypes._node import NodeBase, register_node
from . import _api_internal
from . import tensor as _tensor
from . import expr as _expr
from . import collections as _collections
from ._ctypes._function import _init_api
@register_node
class Buffer(NodeBase):
"""Represent a Buffer in TVM."""
"""Represent a symbolic buffer in TVM."""
pass
@register_node
......@@ -20,6 +23,29 @@ class Fuse(NodeBase):
"""Fuse operation on axis."""
pass
@register_node
class IterVar(NodeBase, _expr.ExprOp):
"""Represent iteration variable.
IterVar is normally created by Operation, to represent
axis iterations in the computation.
It can also created by schedule primitives like :any:`tvm.schedule.Stage.split`.
See Also
--------
tvm.thread_axis: Create thread axis IterVar.
tvm.reduce_axis: Create reduce axis IterVar.
"""
DataPar = 0
ThreadIndex = 1
CommReduce = 2
Ordered = 3
DimInfo = 4
Unrolled = 5
Vectorized = 6
Parallelized = 7
_tensor.iter_var_cls = IterVar
def create_schedule(ops):
"""Create a schedule for list of ops
......@@ -343,3 +369,5 @@ class Stage(NodeBase):
The iteration to be parallelized.
"""
_api_internal._StageParallel(self, var)
_init_api("tvm.schedule")
python/tvm/stmt.py
"""Statement classes"""
"""Statement AST Node in TVM.
User do not need to deal with AST node directly.
But they can be helpful for developer to do quick proptyping.
While not displayed in the document and python file.
Each statement node have subfields that can be visited from python side.
.. code-block:: python
x = tvm.var("n")
a = tvm.var("array", tvm.handle)
st = tvm.make.Store(a, x + 1, 1)
assert isinstance(st, tvm.stmt.Store)
assert(st.buffer_var == a)
"""
from __future__ import absolute_import as _abs
from ._ctypes._node import NodeBase, register_node
......
python/tvm/tensor.py
"""Tensor related abstractions"""
"""Tensor and Computation abstraction objects"""
# pylint: disable=invalid-name
from __future__ import absolute_import as _abs
from ._ctypes._node import NodeBase, SliceBase, register_node, convert_to_node
from . import collections as _collections
from . import _api_internal
from . import make as _make
from . import expr as _expr
......@@ -19,6 +19,7 @@ class TensorSlice(SliceBase, _expr.ExprOp):
indices = (indices,)
return TensorSlice(self.tensor, self.indices + indices)
itervar_cls = None
@register_node
class Tensor(NodeBase):
......@@ -30,10 +31,10 @@ class Tensor(NodeBase):
indices = convert_to_node(indices)
args = []
for x in indices:
if isinstance(x, _collections.IterVar):
args.append(x.var)
elif isinstance(x, _expr.Expr):
if isinstance(x, _expr.Expr):
args.append(x)
elif isinstance(x, iter_var_cls):
args.append(x.var)
else:
raise ValueError("The indices must be expression")
......@@ -95,20 +96,35 @@ class Operation(NodeBase):
"""
return _api_internal._OpGetOutput(self, index)
@register_node
class PlaceholderOp(Operation):
"""Placeholder operation."""
pass
@register_node
class ComputeOp(Operation):
"""Compute operation."""
pass
@property
def axis(self):
"""Represent axis of IterVar, only defined when it is a ComputeOp"""
return self.__getattr__("axis")
@property
def reduce_axis(self):
"""Represent axis of reductions, only defined when it is a ComputeOp"""
return self.__getattr__("reduce_axis")
@register_node
class ScanOp(Operation):
"""Scan operation."""
pass
@property
def scan_axis(self):
"""Represent axis of scan, only defined when it is a ScanOp"""
return self.__getattr__("scan_axis")
@register_node
class ExternOp(Operation):
......
src/api/api_codegen.cc
......@@ -12,7 +12,7 @@
namespace tvm {
namespace codegen {
TVM_REGISTER_API(_codegen_build)
TVM_REGISTER_API(_codegen__Build)
.set_body([](TVMArgs args, TVMRetValue *ret) {
if (args[0].IsNodeType<LoweredFunc>()) {
*ret = Build({args[0]}, args[1]);
......@@ -21,7 +21,7 @@ TVM_REGISTER_API(_codegen_build)
}
});
TVM_REGISTER_API(_codegen_enabled)
TVM_REGISTER_API(_codegen__Enabled)
.set_body([](TVMArgs args, TVMRetValue *ret) {
*ret = TargetEnabled(args[0]);
});
......
src/runtime/module.cc
......@@ -97,7 +97,7 @@ bool RuntimeEnabled(const std::string& target) {
return runtime::Registry::Get(load_f_name) != nullptr;
}
TVM_REGISTER_GLOBAL(_module_enabled)
TVM_REGISTER_GLOBAL(_module__Enabled)
.set_body([](TVMArgs args, TVMRetValue *ret) {
*ret = RuntimeEnabled(args[0]);
});
......
tests/python/integration/test_dot.py
......@@ -38,7 +38,7 @@ def test_dot():
if not tvm.codegen.enabled(target):
print("Target %s is not enabled" % target)
return
f = tvm.codegen.build(fapi, target)
f = tvm.codegen.build_module(fapi, target)
# verify
ctx = tvm.cpu(0)
a = tvm.nd.array(np.random.uniform(size=(nn,)).astype(A.dtype), ctx)
......
tests/python/unittest/test_codegen_device.py
......@@ -32,8 +32,8 @@ def test_add_pipeline():
if not tvm.codegen.enabled(device):
return
ctx = tvm.gpu(0) if device == "cuda" else tvm.cl(0)
mhost = tvm.codegen.build(fsplits[0], host)
mdev = tvm.codegen.build(fsplits[1:], device)
mhost = tvm.codegen.build_module(fsplits[0], host)
mdev = tvm.codegen.build_module(fsplits[1:], device)
mhost.import_module(mdev)
code = mdev.get_source()
f = mhost.entry_func
......@@ -54,8 +54,8 @@ def test_add_pipeline():
return
ctx = tvm.gpu(0) if device == "cuda" else tvm.cl(0)
fmt = "ptx" if device == "cuda" else "cl"
mhost = tvm.codegen.build(fsplits[0], host)
mdev = tvm.codegen.build(fsplits[1:], device)
mhost = tvm.codegen.build_module(fsplits[0], host)
mdev = tvm.codegen.build_module(fsplits[1:], device)
temp = testing.tempdir()
mpath = temp.relpath("test.%s" % fmt)
mdev.save(mpath)
......
tests/python/unittest/test_codegen_vm_basic.py
......@@ -5,7 +5,7 @@ def run_jit(fapi, check):
for target in ["llvm", "stackvm"]:
if not tvm.codegen.enabled(target):
continue
f = tvm.codegen.build(fapi, target)
f = tvm.codegen.build_module(fapi, target)
s = f.get_source()
check(f)
......
tests/python/unittest/test_lang_basic.py
......@@ -29,6 +29,13 @@ def test_ir():
stmt = tvm.make.Evaluate(z)
assert isinstance(stmt, tvm.stmt.Evaluate)
def test_ir2():
x = tvm.var("n")
a = tvm.var("array", tvm.handle)
st = tvm.make.Store(a, x + 1, 1)
assert isinstance(st, tvm.stmt.Store)
assert(st.buffer_var == a)
def test_let():
x = tvm.var('x')
y = tvm.var('y')
......
tests/python/unittest/test_lang_schedule.py
......@@ -86,8 +86,8 @@ def test_vectorize():
xo, yo, xi, yi = s[T].tile(T.op.axis[0], T.op.axis[1], x_factor=10, y_factor=5)
s[T].vectorize(yi)
s[T].unroll(xi)
UNROLL = tvm.collections.IterVar.Unrolled
VECTORIZE = tvm.collections.IterVar.Vectorized
UNROLL = tvm.schedule.IterVar.Unrolled
VECTORIZE = tvm.schedule.IterVar.Vectorized
assert s[T].iter_var_attrs[xi].iter_type == UNROLL
assert s[T].iter_var_attrs[yi].iter_type == VECTORIZE
......
tests/python/unittest/test_module_load.py
......@@ -36,7 +36,7 @@ def test_dso_module_load():
tvm.make.Load(dtype, Ab.data, i) + 1,
i + 1))
fapi = tvm.ir_pass.MakeAPI(stmt, "ramp", [Ab], 0)
m = tvm.codegen.build(fapi, "llvm")
m = tvm.codegen.build_module(fapi, "llvm")
for name in names:
m.save(name)
......
tests/verilog/integration/test_codegen_verilog.py
......@@ -48,8 +48,8 @@ def test_add_pipeline():
if not tvm.codegen.enabled(device):
return
ctx = tvm.vpi(0)
mhost = tvm.codegen.build(fsplits[0], host)
mdev = tvm.codegen.build(fsplits[1:], device)
mhost = tvm.codegen.build_module(fsplits[0], host)
mdev = tvm.codegen.build_module(fsplits[1:], device)
mhost.import_module(mdev)
code = mdev.get_source()
f = mhost.entry_func
......
tutorials/python/README.txt
.. _tutorials-index:
TVM Tutorials
=============
.. _notebook_examples:
Tutorials
=========
These tutorials are tutorial generated by sphinx-gallery.
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