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[DOC] Initial doc system (#88) 

* [DOC] Initial doc system

* Migrate API

* Update docs
parent 4d280905
Showing with 1146 additions and 283 deletions
README.md
[![Build Status](https://travis-ci.com/tqchen/tvm.svg?token=ZQpnpAReT4LHdjWAX8jR&branch=master)](https://travis-ci.com/tqchen/tvm) [![Build Status](https://travis-ci.com/tqchen/tvm.svg?token=ZQpnpAReT4LHdjWAX8jR&branch=master)](https://travis-ci.com/tqchen/tvm)
[![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)
[Installation](docs/how_to/install.md) | [Installation](docs/how_to/install.md) |
[Documentation](docs) |
[Tutorials](tutorials) |
[Contributor Guide](docs/how_to/contribute.md) | [Contributor Guide](docs/how_to/contribute.md) |
[Release Notes](NEWS.md) [Release Notes](NEWS.md)
......
docs/.gitignore
doxygen doxygen
modules
tutorials
docs/Makefile 0 → 100644
# Makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
PAPER =
BUILDDIR = _build
# User-friendly check for sphinx-build
ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
endif
# Internal variables.
PAPEROPT_a4 = -D latex_paper_size=a4
PAPEROPT_letter = -D latex_paper_size=letter
ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
# the i18n builder cannot share the environment and doctrees with the others
I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext
help:
@echo "Please use \`make <target>' where <target> is one of"
@echo " html to make standalone HTML files"
@echo " dirhtml to make HTML files named index.html in directories"
@echo " singlehtml to make a single large HTML file"
@echo " pickle to make pickle files"
@echo " json to make JSON files"
@echo " htmlhelp to make HTML files and a HTML help project"
@echo " qthelp to make HTML files and a qthelp project"
@echo " applehelp to make an Apple Help Book"
@echo " devhelp to make HTML files and a Devhelp project"
@echo " epub to make an epub"
@echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
@echo " latexpdf to make LaTeX files and run them through pdflatex"
@echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
@echo " text to make text files"
@echo " man to make manual pages"
@echo " texinfo to make Texinfo files"
@echo " info to make Texinfo files and run them through makeinfo"
@echo " gettext to make PO message catalogs"
@echo " changes to make an overview of all changed/added/deprecated items"
@echo " xml to make Docutils-native XML files"
@echo " pseudoxml to make pseudoxml-XML files for display purposes"
@echo " linkcheck to check all external links for integrity"
@echo " doctest to run all doctests embedded in the documentation (if enabled)"
@echo " coverage to run coverage check of the documentation (if enabled)"
clean:
rm -rf $(BUILDDIR)/*
html:
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
dirhtml:
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
singlehtml:
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
@echo
@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
pickle:
$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
@echo
@echo "Build finished; now you can process the pickle files."
json:
$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
@echo
@echo "Build finished; now you can process the JSON files."
htmlhelp:
$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
@echo
@echo "Build finished; now you can run HTML Help Workshop with the" \
".hhp project file in $(BUILDDIR)/htmlhelp."
qthelp:
$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
@echo
@echo "Build finished; now you can run "qcollectiongenerator" with the" \
".qhcp project file in $(BUILDDIR)/qthelp, like this:"
@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/rabit.qhcp"
@echo "To view the help file:"
@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/rabit.qhc"
applehelp:
$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
@echo
@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
@echo "N.B. You won't be able to view it unless you put it in" \
"~/Library/Documentation/Help or install it in your application" \
"bundle."
devhelp:
$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
@echo
@echo "Build finished."
@echo "To view the help file:"
@echo "# mkdir -p $$HOME/.local/share/devhelp/rabit"
@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/rabit"
@echo "# devhelp"
epub:
$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
@echo
@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
latex:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo
@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
@echo "Run \`make' in that directory to run these through (pdf)latex" \
"(use \`make latexpdf' here to do that automatically)."
latexpdf:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo "Running LaTeX files through pdflatex..."
$(MAKE) -C $(BUILDDIR)/latex all-pdf
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
latexpdfja:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo "Running LaTeX files through platex and dvipdfmx..."
$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
text:
$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
@echo
@echo "Build finished. The text files are in $(BUILDDIR)/text."
man:
$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
@echo
@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
texinfo:
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
@echo
@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
@echo "Run \`make' in that directory to run these through makeinfo" \
"(use \`make info' here to do that automatically)."
info:
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
@echo "Running Texinfo files through makeinfo..."
make -C $(BUILDDIR)/texinfo info
@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
gettext:
$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
@echo
@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
changes:
$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
@echo
@echo "The overview file is in $(BUILDDIR)/changes."
linkcheck:
$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
@echo
@echo "Link check complete; look for any errors in the above output " \
"or in $(BUILDDIR)/linkcheck/output.txt."
doctest:
$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
@echo "Testing of doctests in the sources finished, look at the " \
"results in $(BUILDDIR)/doctest/output.txt."
coverage:
$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
@echo "Testing of coverage in the sources finished, look at the " \
"results in $(BUILDDIR)/coverage/python.txt."
xml:
$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
@echo
@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
pseudoxml:
$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
@echo
@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
docs/README.txt 0 → 100644
The documentation of tvm is generated with recommonmark and sphinx.
- pip install sphinx sphinx-gallery sphinx_rtd_theme matplotlib Image recommonmark
You can build it locally by typing "make html" in this folder.
docs/conf.py 0 → 100644
# -*- coding: utf-8 -*-
#
# documentation build configuration file, created by
# sphinx-quickstart on Thu Jul 23 19:40:08 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
import os, subprocess
import shlex
import recommonmark
import sphinx_gallery
from recommonmark.parser import CommonMarkParser
from recommonmark.transform import AutoStructify
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
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.
project = u'tvm'
author = u'%s developers' % project
copyright = u'2017, %s' % author
github_doc_root = 'https://github.com/tqchen/tvm/tree/master/docs/'
# add markdown parser
CommonMarkParser.github_doc_root = github_doc_root
source_parsers = {
'.md': CommonMarkParser
}
os.environ['TVM_BUILD_DOC'] = '1'
# Version information.
import tvm
version = tvm.__version__
release = tvm.__version__
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.napoleon',
'sphinx.ext.mathjax',
'sphinx_gallery.gen_gallery',
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
# source_suffix = ['.rst', '.md']
source_suffix = ['.rst', '.md']
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# generate autosummary even if no references
autosummary_generate = True
# The master toctree document.
master_doc = 'index'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# If true, keep warnings as "system message" paragraphs in the built documents.
#keep_warnings = False
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False
# -- Options for HTML output ----------------------------------------------
# The theme is set by the make target
html_theme = os.environ.get('TVM_THEME', 'rtd')
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
# only import rtd theme and set it if want to build docs locally
if not on_rtd and html_theme == 'rtd':
import sphinx_rtd_theme
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# 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']
# Output file base name for HTML help builder.
htmlhelp_basename = project + 'doc'
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, '%s.tex' % project, project,
author, 'manual'),
]
# hook for doxygen
def run_doxygen(folder):
"""Run the doxygen make command in the designated folder."""
try:
retcode = subprocess.call("cd %s; make doxygen" % 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)
examples_dirs = ['../tutorials/python']
gallery_dirs = ['tutorials']
def generate_doxygen_xml(app):
"""Run the doxygen make commands if we're on the ReadTheDocs server"""
read_the_docs_build = os.environ.get('READTHEDOCS', None) == 'True'
if read_the_docs_build:
run_doxygen('..')
def setup(app):
# Add hook for building doxygen xml when needed
# no c++ API for now
# app.connect("builder-inited", generate_doxygen_xml)
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
},
'examples_dirs': examples_dirs,
'gallery_dirs': gallery_dirs
}
docs/how_to/install.md
...@@ -8,7 +8,7 @@ scratch on various systems. It consists of two steps: ...@@ -8,7 +8,7 @@ scratch on various systems. It consists of two steps:
To get started, clone tvm repo from github. It is important to clone the submodules along, with ```--recursive``` option. To get started, clone tvm repo from github. It is important to clone the submodules along, with ```--recursive``` option.
```bash ```bash
git clone --recursive https://github.com/tqchen/tvm git clone --recursive ssh://git@github.com/tqchen/tvm
``` ```
For windows users who use github tools, you can open the git shell, and type the following command. For windows users who use github tools, you can open the git shell, and type the following command.
```bash ```bash
......
docs/index.rst 0 → 100644
TVM Documentation
=================
Welcome to TVM documentation.
Contents
--------
.. toctree::
:maxdepth: 1
how_to/contribute
how_to/install
tutorials/index
python/api
docs/python/api.rst 0 → 100644
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/__init__.py
# pylint: disable=redefined-builtin, wildcard-import # pylint: disable=redefined-builtin, wildcard-import
"""C++ backend related python scripts""" """C++ backend related python scripts"""
from __future__ import absolute_import as _abs from __future__ import absolute_import as _abs
from ._ctypes._node import register_node
from . import tensor from . import tensor
from . import arith from . import arith
...@@ -13,10 +12,16 @@ from . import codegen ...@@ -13,10 +12,16 @@ from . import codegen
from . import collections from . import collections
from . import schedule from . import schedule
from . import module from . import module
from . import node
from . import ndarray as nd from . import ndarray as nd
from .ndarray import cpu, gpu, opencl, cl, vpi from .ndarray import cpu, gpu, opencl, cl, vpi
from ._ctypes._function import Function
from ._base import TVMError from ._base import TVMError
from ._base import __version__
from .api import * from .api import *
from .node import register_node
from .schedule import create_schedule
from .build import build, lower from .build import build, lower
python/tvm/_ctypes/_function.py
...@@ -136,7 +136,8 @@ def _make_tvm_args(args, temp_args): ...@@ -136,7 +136,8 @@ def _make_tvm_args(args, temp_args):
class Function(object): class Function(object):
"""A function object at runtime.""" """The Function object used in TVM.
"""
__slots__ = ["handle", "is_global"] __slots__ = ["handle", "is_global"]
# pylint: disable=no-member # pylint: disable=no-member
def __init__(self, handle, is_global=False): def __init__(self, handle, is_global=False):
...@@ -158,6 +159,11 @@ class Function(object): ...@@ -158,6 +159,11 @@ class Function(object):
check_call(_LIB.TVMFuncFree(self.handle)) check_call(_LIB.TVMFuncFree(self.handle))
def __call__(self, *args): def __call__(self, *args):
"""Call the function with positional arguments
args : list
The positional arguments to the function call.
"""
temp_args = [] temp_args = []
values, tcodes, num_args = _make_tvm_args(args, temp_args) values, tcodes, num_args = _make_tvm_args(args, temp_args)
ret_val = TVMValue() ret_val = TVMValue()
......
python/tvm/_ctypes/_node.py
...@@ -42,7 +42,7 @@ class SliceBase(object): ...@@ -42,7 +42,7 @@ class SliceBase(object):
pass pass
class NodeBase(object): class NodeBase(object):
"""Symbol is symbolic graph.""" """NodeBase is the base class of all TVM language AST object."""
__slots__ = ["handle"] __slots__ = ["handle"]
# pylint: disable=no-member # pylint: disable=no-member
def __init__(self, handle): def __init__(self, handle):
...@@ -119,7 +119,21 @@ class NodeBase(object): ...@@ -119,7 +119,21 @@ class NodeBase(object):
def const(value, dtype=None): def const(value, dtype=None):
"""construct a constant""" """Construct a constant value for a given type.
Parameters
----------
value : int or float
The input value
dtype : str
The data type.
Returns
-------
expr : Expr
Constant expression corresponds to the value.
"""
if dtype is None: if dtype is None:
if isinstance(value, Integral): if isinstance(value, Integral):
dtype = 'int32' dtype = 'int32'
......
python/tvm/addon/testing.py
...@@ -22,6 +22,9 @@ class TempDirectory(object): ...@@ -22,6 +22,9 @@ class TempDirectory(object):
""" """
return os.path.join(self.temp_dir, name) return os.path.join(self.temp_dir, name)
def listdir(self):
""""List contents in the dir"""
return os.listdir(self.temp_dir)
def tempdir(): def tempdir():
"""Return a new temp dir which deletes the contents when exit """Return a new temp dir which deletes the contents when exit
......
python/tvm/api.py
...@@ -63,7 +63,7 @@ def save_json(node): ...@@ -63,7 +63,7 @@ def save_json(node):
return _api_internal._save_json(node) return _api_internal._save_json(node)
def Var(name="tindex", dtype=int32): def var(name="tindex", dtype=int32):
"""Create a new variable with specified name and dtype """Create a new variable with specified name and dtype
Parameters Parameters
...@@ -73,6 +73,11 @@ def Var(name="tindex", dtype=int32): ...@@ -73,6 +73,11 @@ def Var(name="tindex", dtype=int32):
dtype : int dtype : int
The data type The data type
Returns
-------
var : Var
The result symbolic variable.
""" """
return _api_internal._Var(name, dtype) return _api_internal._Var(name, dtype)
...@@ -93,7 +98,7 @@ def placeholder(shape, dtype=None, name="placeholder"): ...@@ -93,7 +98,7 @@ def placeholder(shape, dtype=None, name="placeholder"):
Returns Returns
------- -------
tensor: tensor.Tensor tensor: Tensor
The created tensor The created tensor
""" """
shape = (shape,) if isinstance(shape, _expr.Expr) else shape shape = (shape,) if isinstance(shape, _expr.Expr) else shape
...@@ -112,7 +117,7 @@ def compute(shape, fcompute, name="compute"): ...@@ -112,7 +117,7 @@ def compute(shape, fcompute, name="compute"):
shape: Tuple of Expr shape: Tuple of Expr
The shape of the tensor The shape of the tensor
fcompute: lambda function of *indices-> value fcompute: lambda function of indices-> value
Specifies the input source expression Specifies the input source expression
name: str, optional name: str, optional
...@@ -120,7 +125,7 @@ def compute(shape, fcompute, name="compute"): ...@@ -120,7 +125,7 @@ def compute(shape, fcompute, name="compute"):
Returns Returns
------- -------
tensor: tensor.Tensor tensor: Tensor
The created tensor The created tensor
""" """
shape = (shape,) if isinstance(shape, _expr.Expr) else shape shape = (shape,) if isinstance(shape, _expr.Expr) else shape
...@@ -171,14 +176,16 @@ def scan(init, update, state_placeholder, inputs=None, name="scan"): ...@@ -171,14 +176,16 @@ def scan(init, update, state_placeholder, inputs=None, name="scan"):
Example Example
------- -------
# The following code is equivalent to numpy.cumsum .. code-block:: python
m = tvm.Var("m")
n = tvm.Var("n") # The following code is equivalent to numpy.cumsum
X = tvm.placeholder((m, n), name="X") m = tvm.Var("m")
s_state = tvm.placeholder((m, n)) n = tvm.Var("n")
s_init = tvm.compute((1, n), lambda _, i: X[0, i]) X = tvm.placeholder((m, n), name="X")
s_update = tvm.compute((m, n), lambda t, i: s_state[t-1, i] + X[t, i]) s_state = tvm.placeholder((m, n))
res = tvm.scan(s_init, s_update, s_state, X) s_init = tvm.compute((1, n), lambda _, i: X[0, i])
s_update = tvm.compute((m, n), lambda t, i: s_state[t-1, i] + X[t, i])
res = tvm.scan(s_init, s_update, s_state, X)
""" """
if isinstance(init, _tensor.Tensor): if isinstance(init, _tensor.Tensor):
init = [init] init = [init]
...@@ -234,7 +241,7 @@ def extern(shape, inputs, fcompute, ...@@ -234,7 +241,7 @@ def extern(shape, inputs, fcompute,
if not isinstance(t, _tensor.Tensor): if not isinstance(t, _tensor.Tensor):
raise ValueError("expect inputs to be tensor") raise ValueError("expect inputs to be tensor")
input_placeholders.append( input_placeholders.append(
Buffer(t.shape, t.dtype, t.op.name)) decl_buffer(t.shape, t.dtype, t.op.name))
types.add(t.dtype) types.add(t.dtype)
if dtype is None: if dtype is None:
...@@ -244,7 +251,7 @@ def extern(shape, inputs, fcompute, ...@@ -244,7 +251,7 @@ def extern(shape, inputs, fcompute,
dtype = [infered_type for _ in shape] dtype = [infered_type for _ in shape]
for shp, dt in zip(shape, dtype): for shp, dt in zip(shape, dtype):
output_placeholders.append(Buffer(shp, dt, name)) output_placeholders.append(decl_buffer(shp, dt, name))
body = fcompute(input_placeholders, output_placeholders) body = fcompute(input_placeholders, output_placeholders)
if isinstance(body, _expr.Expr): if isinstance(body, _expr.Expr):
body = _make.Evaluate(body) body = _make.Evaluate(body)
...@@ -268,13 +275,13 @@ def call_packed(*args): ...@@ -268,13 +275,13 @@ def call_packed(*args):
int32, "tvm_call_packed", args, 4, None, 0) int32, "tvm_call_packed", args, 4, None, 0)
def Buffer(shape, dtype=None, def decl_buffer(shape, dtype=None,
name="buffer", name="buffer",
data=None, data=None,
strides=None, strides=None,
byte_offset=None, byte_offset=None,
offset_alignment=0): offset_alignment=0):
"""Create a new symbolic buffer """Decleare a new symbolic buffer
Parameters Parameters
---------- ----------
...@@ -308,7 +315,7 @@ def Buffer(shape, dtype=None, ...@@ -308,7 +315,7 @@ def Buffer(shape, dtype=None,
dtype = float32 if dtype is None else dtype dtype = float32 if dtype is None else dtype
strides = () if strides is None else strides strides = () if strides is None else strides
if data is None: if data is None:
data = Var(name, "handle") data = var(name, "handle")
return _api_internal._Buffer( return _api_internal._Buffer(
name, data, shape, strides, dtype, byte_offset, offset_alignment) name, data, shape, strides, dtype, byte_offset, offset_alignment)
...@@ -345,8 +352,8 @@ def _IterVar(dom, name, iter_type, thread_tag=''): ...@@ -345,8 +352,8 @@ def _IterVar(dom, name, iter_type, thread_tag=''):
if not isinstance(dom, _collections.Range): if not isinstance(dom, _collections.Range):
raise ValueError("dom need to be Range") raise ValueError("dom need to be Range")
name = name if name else 'iter' name = name if name else 'iter'
var = Var(name) v = var(name)
return _api_internal._IterVar(dom, var, iter_type, thread_tag) return _api_internal._IterVar(dom, v, iter_type, thread_tag)
def thread_axis(dom=None, tag='', name=''): def thread_axis(dom=None, tag='', name=''):
...@@ -382,6 +389,11 @@ def reduce_axis(dom, name="rv"): ...@@ -382,6 +389,11 @@ def reduce_axis(dom, name="rv"):
name : str name : str
The name of the variable. The name of the variable.
Returns
-------
axis : IterVar
An iteration variable representing the value.
""" """
return _IterVar(dom, name, 2) return _IterVar(dom, name, 2)
...@@ -399,6 +411,11 @@ def sum(expr, axis, where=None): ...@@ -399,6 +411,11 @@ def sum(expr, axis, where=None):
where : optional, Expr where : optional, Expr
Filtering predicate of the reduction. Filtering predicate of the reduction.
Returns
-------
value : Expr
The result value.
""" """
axis = axis if isinstance(axis, list) else [axis] axis = axis if isinstance(axis, list) else [axis]
x = _make.Reduce("Add", expr, axis, where) x = _make.Reduce("Add", expr, axis, where)
...@@ -421,6 +438,11 @@ def min(lhs, rhs=None, axis=None, where=None): ...@@ -421,6 +438,11 @@ def min(lhs, rhs=None, axis=None, where=None):
where : optional, Expr where : optional, Expr
Filtering predicate of the reduction. Filtering predicate of the reduction.
Returns
-------
value : Expr
The result value.
""" """
if rhs and axis: if rhs and axis:
raise ValueError("Can only take one argument, rhs or axis") raise ValueError("Can only take one argument, rhs or axis")
...@@ -449,6 +471,11 @@ def max(lhs, rhs=None, axis=None, where=None): ...@@ -449,6 +471,11 @@ def max(lhs, rhs=None, axis=None, where=None):
where : optional, Expr where : optional, Expr
Filtering predicate of the reduction. Filtering predicate of the reduction.
Returns
-------
value : Expr
The result value.
""" """
if rhs and axis: if rhs and axis:
raise ValueError("Can only take one argument, rhs or axis") raise ValueError("Can only take one argument, rhs or axis")
...@@ -461,19 +488,6 @@ def max(lhs, rhs=None, axis=None, where=None): ...@@ -461,19 +488,6 @@ def max(lhs, rhs=None, axis=None, where=None):
return x return x
def Schedule(ops):
"""Create a schedule for list of ops
Parameters
----------
ops : list of Operations
The source expression.
"""
if not isinstance(ops, (list, _collections.Array)):
ops = [ops]
return _api_internal._Schedule(ops)
def convert(value): def convert(value):
"""Convert value to TVM node or function. """Convert value to TVM node or function.
...@@ -483,7 +497,7 @@ def convert(value): ...@@ -483,7 +497,7 @@ def convert(value):
Returns Returns
------- -------
tvm_val : Node or function tvm_val : Node or Function
Converted value in TVM Converted value in TVM
""" """
if isinstance(value, (Function, NodeBase)): if isinstance(value, (Function, NodeBase)):
......
python/tvm/build.py
...@@ -46,7 +46,7 @@ def lower(sch, ...@@ -46,7 +46,7 @@ def lower(sch,
arg_list = [] arg_list = []
for x in args: for x in args:
if isinstance(x, tensor.Tensor): if isinstance(x, tensor.Tensor):
buf = api.Buffer(x.shape, dtype=x.dtype, name=x.op.name) buf = api.decl_buffer(x.shape, dtype=x.dtype, name=x.op.name)
assert x not in binds assert x not in binds
binds[x] = buf binds[x] = buf
arg_list.append(buf) arg_list.append(buf)
......
python/tvm/expr.py
"""Expression class""" """Module to declare Expression class"""
# pylint: disable=missing-docstring # pylint: disable=missing-docstring
from __future__ import absolute_import as _abs from __future__ import absolute_import as _abs
from ._ctypes._node import NodeBase, register_node from ._ctypes._node import NodeBase, register_node
...@@ -58,6 +58,7 @@ class ExprOp(object): ...@@ -58,6 +58,7 @@ class ExprOp(object):
class Expr(NodeBase, ExprOp): class Expr(NodeBase, ExprOp):
"""Base class of all tvm Expressions"""
pass pass
class ConstExpr(Expr): class ConstExpr(Expr):
...@@ -72,10 +73,9 @@ class CmpExpr(Expr): ...@@ -72,10 +73,9 @@ class CmpExpr(Expr):
class LogicalExpr(Expr): class LogicalExpr(Expr):
pass pass
@register_node("Variable") @register_node("Variable")
class Var(Expr): class Var(Expr):
"""Symbolic variable expression."""
pass pass
@register_node @register_node
......
python/tvm/ndarray.py
...@@ -9,7 +9,6 @@ import numpy as _np ...@@ -9,7 +9,6 @@ import numpy as _np
from ._ctypes._ndarray import TVMContext, TVMType, NDArrayBase from ._ctypes._ndarray import TVMContext, TVMType, NDArrayBase
from ._ctypes._ndarray import cpu, gpu, opencl, vpi, empty, sync from ._ctypes._ndarray import cpu, gpu, opencl, vpi, empty, sync
from ._ctypes._ndarray import _init_ndarray_module from ._ctypes._ndarray import _init_ndarray_module
from ._ctypes._function import Function
cl = opencl cl = opencl
...@@ -40,7 +39,7 @@ def array(arr, ctx=cpu(0)): ...@@ -40,7 +39,7 @@ def array(arr, ctx=cpu(0)):
Returns Returns
------- -------
ret : tvm.nd.NDArray ret : NDArray
The created array The created array
""" """
if not isinstance(arr, _np.ndarray): if not isinstance(arr, _np.ndarray):
......
python/tvm/node.py 0 → 100644
"""Namespace for base node class"""
# pylint: disable=unused-import
from __future__ import absolute_import as _abs
from ._ctypes._node import NodeBase, register_node
Node = NodeBase
python/tvm/schedule.py
...@@ -21,6 +21,24 @@ class Fuse(NodeBase): ...@@ -21,6 +21,24 @@ class Fuse(NodeBase):
pass pass
def create_schedule(ops):
"""Create a schedule for list of ops
Parameters
----------
ops : list of Operations
The source expression.
Returns
-------
sch : schedule.Schedule
The created schedule.
"""
if not isinstance(ops, (list, _collections.Array)):
ops = [ops]
return _api_internal._Schedule(ops)
@register_node @register_node
class Schedule(NodeBase): class Schedule(NodeBase):
"""Schedule for all the stages.""" """Schedule for all the stages."""
...@@ -278,7 +296,8 @@ class Stage(NodeBase): ...@@ -278,7 +296,8 @@ class Stage(NodeBase):
The original y dimension The original y dimension
x_factor : Expr x_factor : Expr
The stride factor on x axis The stride factor on x axis
y_factor : Expr The stride factor on y axis y_factor : Expr
The stride factor on y axis
Returns Returns
------- -------
......
python/tvm/tensor.py
...@@ -57,6 +57,26 @@ class Tensor(NodeBase): ...@@ -57,6 +57,26 @@ class Tensor(NodeBase):
"""Dimension of the tensor.""" """Dimension of the tensor."""
return len(self.shape) return len(self.shape)
@property
def axis(self):
"""Axis of the tensor."""
return self.__getattr__("axis")
@property
def op(self):
"""The corressponding :any:`Operation`."""
return self.__getattr__("op")
@property
def value_index(self):
"""The output value index the tensor corressponds to."""
return self.__getattr__("value_index")
@property
def shape(self):
"""The output shape of the tensor."""
return self.__getattr__("shape")
class Operation(NodeBase): class Operation(NodeBase):
"""Represent an operation that generate a tensor""" """Represent an operation that generate a tensor"""
......
src/codegen/codegen_c.cc
...@@ -61,7 +61,7 @@ void CodeGenC::AddFunction(LoweredFunc f) { ...@@ -61,7 +61,7 @@ void CodeGenC::AddFunction(LoweredFunc f) {
} }
std::string CodeGenC::Finish() { std::string CodeGenC::Finish() {
return stream.str(); return decl_stream.str() + stream.str();
} }
void CodeGenC::PrintExpr(const Expr& n, std::ostream& os) { // NOLINT(*) void CodeGenC::PrintExpr(const Expr& n, std::ostream& os) { // NOLINT(*)
......
tests/python/integration/test_dot.py
...@@ -7,7 +7,7 @@ def lower(s, args, name="mydot"): ...@@ -7,7 +7,7 @@ def lower(s, args, name="mydot"):
for x in args: for x in args:
assert isinstance(x, tvm.tensor.Tensor) assert isinstance(x, tvm.tensor.Tensor)
buf = tvm.Buffer(x.shape, dtype=x.dtype, name=x.op.name) buf = tvm.decl_buffer(x.shape, dtype=x.dtype, name=x.op.name)
binds[x] = buf binds[x] = buf
arg_list.append(buf) arg_list.append(buf)
s.normalize() s.normalize()
...@@ -31,7 +31,7 @@ def test_dot(): ...@@ -31,7 +31,7 @@ def test_dot():
B = tvm.placeholder((n,), name='B') B = tvm.placeholder((n,), name='B')
k = tvm.reduce_axis((0, n), 'k') k = tvm.reduce_axis((0, n), 'k')
C = tvm.compute((1,), lambda _: tvm.sum(A[k] * B[k], axis=k), name='C') C = tvm.compute((1,), lambda _: tvm.sum(A[k] * B[k], axis=k), name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
fapi = lower(s, [A, B, C]) fapi = lower(s, [A, B, C])
def verify(target): def verify(target):
......
tests/python/integration/test_ewise.py
...@@ -8,7 +8,7 @@ def test_add(): ...@@ -8,7 +8,7 @@ def test_add():
B = tvm.placeholder((n,), name='B') B = tvm.placeholder((n,), name='B')
C = tvm.compute(A.shape, lambda *i: A(*i) + B(*i), name='C') C = tvm.compute(A.shape, lambda *i: A(*i) + B(*i), name='C')
# schedule # schedule
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
# create iter var and assign them tags. # create iter var and assign them tags.
num_thread = 256 num_thread = 256
bx, x = s[C].split(C.op.axis[0], factor=num_thread*4) bx, x = s[C].split(C.op.axis[0], factor=num_thread*4)
......
tests/python/integration/test_gemm.py
...@@ -5,7 +5,7 @@ import numpy as np ...@@ -5,7 +5,7 @@ import numpy as np
def test_gemm(): def test_gemm():
# graph # graph
nn = 1024 nn = 1024
n = tvm.Var('n') n = tvm.var('n')
n = tvm.convert(nn) n = tvm.convert(nn)
m = n m = n
l = n l = n
...@@ -17,7 +17,7 @@ def test_gemm(): ...@@ -17,7 +17,7 @@ def test_gemm():
lambda ii, jj: tvm.sum(A[ii, k] * B[jj, k], axis=k), lambda ii, jj: tvm.sum(A[ii, k] * B[jj, k], axis=k),
name='CC') name='CC')
# schedule # schedule
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
xtile, ytile = 32, 32 xtile, ytile = 32, 32
scale = 8 scale = 8
num_thread = 8 num_thread = 8
......
tests/python/integration/test_reduce.py
...@@ -3,13 +3,13 @@ import numpy as np ...@@ -3,13 +3,13 @@ import numpy as np
def test_sum(): def test_sum():
# graph # graph
n = tvm.Var('n') n = tvm.var('n')
m = tvm.Var('m') m = tvm.var('m')
A = tvm.placeholder((n, m), name='A') A = tvm.placeholder((n, m), name='A')
k = tvm.reduce_axis((0, m)) k = tvm.reduce_axis((0, m))
B = tvm.compute((n,), lambda i: tvm.sum(A[i, k], axis=k, where=(i>1)), name='B') B = tvm.compute((n,), lambda i: tvm.sum(A[i, k], axis=k, where=(i>1)), name='B')
# schedule # schedule
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
# create iter var and assign them tags. # create iter var and assign them tags.
num_thread = 1 num_thread = 1
xo, xi = s[B].split(B.op.axis[0], factor=num_thread) xo, xi = s[B].split(B.op.axis[0], factor=num_thread)
...@@ -52,7 +52,7 @@ def test_rfactor(): ...@@ -52,7 +52,7 @@ def test_rfactor():
k = tvm.reduce_axis((0, n)) k = tvm.reduce_axis((0, n))
B = tvm.compute((1,), lambda i: tvm.sum(A[k], axis=k), name='B') B = tvm.compute((1,), lambda i: tvm.sum(A[k], axis=k), name='B')
# schedule # schedule
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
kf, ki = s[B].split(k, nparts=4) kf, ki = s[B].split(k, nparts=4)
BF = s.rfactor(B, kf) BF = s.rfactor(B, kf)
s[BF].parallel(BF.op.axis[0]) s[BF].parallel(BF.op.axis[0])
...@@ -87,7 +87,7 @@ def test_rfactor_threads(): ...@@ -87,7 +87,7 @@ def test_rfactor_threads():
nthread = 16 nthread = 16
B = tvm.compute((m,), lambda i: tvm.sum(A[i, k], axis=k, where=(i>1)), name='B') B = tvm.compute((m,), lambda i: tvm.sum(A[i, k], axis=k, where=(i>1)), name='B')
# schedule # schedule
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
ko, kf = s[B].split(k, factor=nthread) ko, kf = s[B].split(k, factor=nthread)
BF = s.rfactor(B, kf) BF = s.rfactor(B, kf)
bx, ty = s[B].split(s[B].op.axis[0], factor=nthread) bx, ty = s[B].split(s[B].op.axis[0], factor=nthread)
......
tests/python/integration/test_scan.py
...@@ -2,8 +2,8 @@ import tvm ...@@ -2,8 +2,8 @@ import tvm
import numpy as np import numpy as np
def test_scan(): def test_scan():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
X = tvm.placeholder((m, n), name="X") X = tvm.placeholder((m, n), name="X")
s_state = tvm.placeholder((m, n)) s_state = tvm.placeholder((m, n))
s_init = tvm.compute((1, n), lambda _, i: X[0, i]) s_init = tvm.compute((1, n), lambda _, i: X[0, i])
...@@ -11,7 +11,7 @@ def test_scan(): ...@@ -11,7 +11,7 @@ def test_scan():
res = tvm.scan(s_init, s_update, s_state) res = tvm.scan(s_init, s_update, s_state)
# schedule # schedule
s = tvm.Schedule(res.op) s = tvm.create_schedule(res.op)
num_thread = 256 num_thread = 256
block_x = tvm.thread_axis(None, "blockIdx.x") block_x = tvm.thread_axis(None, "blockIdx.x")
thread_x = tvm.thread_axis((0, num_thread), "threadIdx.x") thread_x = tvm.thread_axis((0, num_thread), "threadIdx.x")
......
tests/python/perf/rnn_matexp.py
...@@ -47,7 +47,7 @@ def rnn_matexp(): ...@@ -47,7 +47,7 @@ def rnn_matexp():
max_auto_unroll_step = 0 max_auto_unroll_step = 0
detect_global_barrier = DETECT_GLOBAL_BARRIER detect_global_barrier = DETECT_GLOBAL_BARRIER
num_step = tvm.Var("num_step") num_step = tvm.var("num_step")
num_hidden = tvm.convert(n_num_hidden) num_hidden = tvm.convert(n_num_hidden)
batch_size = tvm.convert(n_batch_size) batch_size = tvm.convert(n_batch_size)
num_thread_y = 8 num_thread_y = 8
...@@ -65,7 +65,7 @@ def rnn_matexp(): ...@@ -65,7 +65,7 @@ def rnn_matexp():
name="update") name="update")
s_scan = tvm.scan(s_init, s_update, s_state) s_scan = tvm.scan(s_init, s_update, s_state)
# schedule # schedule
s = tvm.Schedule(s_scan.op) s = tvm.create_schedule(s_scan.op)
CL = s_update CL = s_update
SS = s.cache_read(s_state, "shared", [CL]) SS = s.cache_read(s_state, "shared", [CL])
SL = s.cache_read(SS, "local", [CL]) SL = s.cache_read(SS, "local", [CL])
......
tests/python/unittest/test_arith_detect_linear_equation.py
import tvm import tvm
def test_basic(): def test_basic():
a = tvm.Var("a") a = tvm.var("a")
b = tvm.Var("b") b = tvm.var("b")
m = tvm.arith.DetectLinearEquation(a * 4 + b * 6 + 7, a) m = tvm.arith.DetectLinearEquation(a * 4 + b * 6 + 7, a)
assert m[1].value == 4 assert m[1].value == 4
assert tvm.ir_pass.Simplify(m[0] - (b * 6 + 7)).value == 0 assert tvm.ir_pass.Simplify(m[0] - (b * 6 + 7)).value == 0
......
tests/python/unittest/test_arith_intset.py
...@@ -6,10 +6,10 @@ def test_basic(): ...@@ -6,10 +6,10 @@ def test_basic():
assert s.max().value == 3 assert s.max().value == 3
def test_deduce(): def test_deduce():
a = tvm.Var('a') a = tvm.var('a')
b = tvm.Var('b') b = tvm.var('b')
c = tvm.Var('c') c = tvm.var('c')
d = tvm.Var('d') d = tvm.var('d')
b_s = tvm.arith.intset_interval(2, 3) b_s = tvm.arith.intset_interval(2, 3)
c_s = tvm.arith.intset_interval(10, 15) c_s = tvm.arith.intset_interval(10, 15)
...@@ -36,10 +36,10 @@ def test_deduce(): ...@@ -36,10 +36,10 @@ def test_deduce():
assert str(tvm.ir_pass.Simplify(res3.min())) == str(ans3) assert str(tvm.ir_pass.Simplify(res3.min())) == str(ans3)
def test_check(): def test_check():
a = tvm.Var('a') a = tvm.var('a')
b = tvm.Var('b') b = tvm.var('b')
c = tvm.Var('c') c = tvm.var('c')
d = tvm.Var('d') d = tvm.var('d')
b_s = tvm.arith.intset_interval(2, 3) b_s = tvm.arith.intset_interval(2, 3)
c_s = tvm.arith.intset_interval(5, 7) c_s = tvm.arith.intset_interval(5, 7)
......
tests/python/unittest/test_arith_modular.py
import tvm import tvm
def test_basic(): def test_basic():
a = tvm.Var() a = tvm.var()
b = tvm.Var() b = tvm.var()
m = tvm.arith.EvalModular(a * 4 + b * 6 + 7) m = tvm.arith.EvalModular(a * 4 + b * 6 + 7)
assert m.coeff == 2 assert m.coeff == 2
assert m.base == 1 assert m.base == 1
......
tests/python/unittest/test_codegen_device.py
...@@ -3,11 +3,11 @@ from tvm.addon import testing ...@@ -3,11 +3,11 @@ from tvm.addon import testing
import numpy as np import numpy as np
def test_add_pipeline(): def test_add_pipeline():
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((n,), name='A') A = tvm.placeholder((n,), name='A')
B = tvm.placeholder((n,), name='B') B = tvm.placeholder((n,), name='B')
C = tvm.compute(A.shape, lambda *i: A(*i) + B(*i), name='C') C = tvm.compute(A.shape, lambda *i: A(*i) + B(*i), name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
# GPU schedule have to split by gridIdx and threadIdx # GPU schedule have to split by gridIdx and threadIdx
num_thread = 256 num_thread = 256
...@@ -18,9 +18,9 @@ def test_add_pipeline(): ...@@ -18,9 +18,9 @@ def test_add_pipeline():
# compile to IR # compile to IR
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
stmt = tvm.schedule.ScheduleOps(s, bounds) stmt = tvm.schedule.ScheduleOps(s, bounds)
Ab = tvm.Buffer(A.shape, A.dtype, name='A') Ab = tvm.decl_buffer(A.shape, A.dtype, name='A')
Bb = tvm.Buffer(B.shape, B.dtype, name='B') Bb = tvm.decl_buffer(B.shape, B.dtype, name='B')
Cb = tvm.Buffer(C.shape, C.dtype, name='C') Cb = tvm.decl_buffer(C.shape, C.dtype, name='C')
stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, B:Bb, C:Cb}) stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, B:Bb, C:Cb})
stmt = tvm.ir_pass.Simplify(stmt) stmt = tvm.ir_pass.Simplify(stmt)
fapi = tvm.ir_pass.MakeAPI(stmt, "myadd", [Ab, Bb, Cb], 0) fapi = tvm.ir_pass.MakeAPI(stmt, "myadd", [Ab, Bb, Cb], 0)
......
tests/python/unittest/test_codegen_extern.py
...@@ -7,7 +7,7 @@ def test_add_pipeline(): ...@@ -7,7 +7,7 @@ def test_add_pipeline():
A = tvm.placeholder((n,), name='A') A = tvm.placeholder((n,), name='A')
def extern_generator(ins, outs): def extern_generator(ins, outs):
"""Manually write the IR for the extern function, add pipeline""" """Manually write the IR for the extern function, add pipeline"""
i = tvm.Var('i') i = tvm.var('i')
stmt = tvm.make.For( stmt = tvm.make.For(
i, 0, n, 0, 0, i, 0, n, 0, 0,
tvm.make.Store(outs[0].data, tvm.make.Store(outs[0].data,
...@@ -15,7 +15,7 @@ def test_add_pipeline(): ...@@ -15,7 +15,7 @@ def test_add_pipeline():
1, i)) 1, i))
return stmt return stmt
C = tvm.extern(A.shape, [A], extern_generator, name='C') C = tvm.extern(A.shape, [A], extern_generator, name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
def check_llvm(): def check_llvm():
if not tvm.codegen.enabled("llvm"): if not tvm.codegen.enabled("llvm"):
......
tests/python/unittest/test_codegen_llvm.py
...@@ -7,7 +7,7 @@ def test_llvm_add_pipeline(): ...@@ -7,7 +7,7 @@ def test_llvm_add_pipeline():
A = tvm.placeholder((n,), name='A') A = tvm.placeholder((n,), name='A')
B = tvm.placeholder((n,), name='B') B = tvm.placeholder((n,), name='B')
C = tvm.compute(A.shape, lambda *i: A(*i) + B(*i), name='C') C = tvm.compute(A.shape, lambda *i: A(*i) + B(*i), name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
xo, xi = s[C].split(C.op.axis[0], factor=4) xo, xi = s[C].split(C.op.axis[0], factor=4)
s[C].parallel(xo) s[C].parallel(xo)
s[C].vectorize(xi) s[C].vectorize(xi)
...@@ -35,7 +35,7 @@ def test_llvm_flip_pipeline(): ...@@ -35,7 +35,7 @@ def test_llvm_flip_pipeline():
n = tvm.convert(nn) n = tvm.convert(nn)
A = tvm.placeholder((n + base), name='A') A = tvm.placeholder((n + base), name='A')
C = tvm.compute((n,), lambda i: A(nn + base- i - 1), name='C') C = tvm.compute((n,), lambda i: A(nn + base- i - 1), name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
xo, xi = s[C].split(C.op.axis[0], factor=4) xo, xi = s[C].split(C.op.axis[0], factor=4)
s[C].parallel(xo) s[C].parallel(xo)
s[C].vectorize(xi) s[C].vectorize(xi)
...@@ -62,7 +62,7 @@ def test_llvm_madd_pipeline(): ...@@ -62,7 +62,7 @@ def test_llvm_madd_pipeline():
n = tvm.convert(nn) n = tvm.convert(nn)
A = tvm.placeholder((n + base, stride), name='A') A = tvm.placeholder((n + base, stride), name='A')
C = tvm.compute((n, stride), lambda i, j: A(base + i, j) + 1, name='C') C = tvm.compute((n, stride), lambda i, j: A(base + i, j) + 1, name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
xo, xi = s[C].split(C.op.axis[0], factor=4) xo, xi = s[C].split(C.op.axis[0], factor=4)
s[C].parallel(xo) s[C].parallel(xo)
s[C].vectorize(xi) s[C].vectorize(xi)
...@@ -86,7 +86,7 @@ def test_llvm_temp_space(): ...@@ -86,7 +86,7 @@ def test_llvm_temp_space():
A = tvm.placeholder((n,), name='A') A = tvm.placeholder((n,), name='A')
B = tvm.compute(A.shape, lambda i: A(i) + 1, name='B') B = tvm.compute(A.shape, lambda i: A(i) + 1, name='B')
C = tvm.compute(A.shape, lambda i: B(i) + 1, name='C') C = tvm.compute(A.shape, lambda i: B(i) + 1, name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
def check_llvm(): def check_llvm():
if not tvm.codegen.enabled("llvm"): if not tvm.codegen.enabled("llvm"):
......
tests/python/unittest/test_codegen_vm_basic.py
...@@ -17,8 +17,8 @@ def test_stack_vm_basic(): ...@@ -17,8 +17,8 @@ def test_stack_vm_basic():
print(shape0) print(shape0)
assert shape0 == a.shape[0] assert shape0 == a.shape[0]
n = tvm.Var('n') n = tvm.var('n')
Ab = tvm.Buffer((n, ), tvm.float32) Ab = tvm.decl_buffer((n, ), tvm.float32)
stmt = tvm.make.Evaluate(tvm.call_packed("tvm_call_back_get_shape", Ab.shape[0])) stmt = tvm.make.Evaluate(tvm.call_packed("tvm_call_back_get_shape", Ab.shape[0]))
fapi = tvm.ir_pass.MakeAPI(stmt, "print_shape", [Ab], 0) fapi = tvm.ir_pass.MakeAPI(stmt, "print_shape", [Ab], 0)
run_jit(fapi, lambda f: f(a)) run_jit(fapi, lambda f: f(a))
...@@ -31,9 +31,9 @@ def tvm_stack_vm_print(*x): ...@@ -31,9 +31,9 @@ def tvm_stack_vm_print(*x):
def test_stack_vm_loop(): def test_stack_vm_loop():
dtype = 'int64' dtype = 'int64'
n = tvm.Var('n') n = tvm.var('n')
Ab = tvm.Buffer((n, ), dtype) Ab = tvm.decl_buffer((n, ), dtype)
i = tvm.Var('i') i = tvm.var('i')
# for i in 0 to n-1: # for i in 0 to n-1:
stmt = tvm.make.For( stmt = tvm.make.For(
i, 0, n - 1, 0, 0, i, 0, n - 1, 0, 0,
...@@ -52,9 +52,9 @@ def test_stack_vm_loop(): ...@@ -52,9 +52,9 @@ def test_stack_vm_loop():
def test_stack_vm_cond(): def test_stack_vm_cond():
dtype = 'int64' dtype = 'int64'
n = tvm.Var('n') n = tvm.var('n')
Ab = tvm.Buffer((n, ), dtype) Ab = tvm.decl_buffer((n, ), dtype)
i = tvm.Var('i') i = tvm.var('i')
# for i in 0 to n-1: # for i in 0 to n-1:
stmt = tvm.make.For( stmt = tvm.make.For(
i, 0, n - 1, 0, 0, i, 0, n - 1, 0, 0,
......
tests/python/unittest/test_lang_basic.py
...@@ -30,14 +30,14 @@ def test_ir(): ...@@ -30,14 +30,14 @@ def test_ir():
assert isinstance(stmt, tvm.stmt.Evaluate) assert isinstance(stmt, tvm.stmt.Evaluate)
def test_let(): def test_let():
x = tvm.Var('x') x = tvm.var('x')
y = tvm.Var('y') y = tvm.var('y')
stmt = tvm.make.LetStmt( stmt = tvm.make.LetStmt(
x, 10, tvm.make.Evaluate(x + 1)); x, 10, tvm.make.Evaluate(x + 1));
def test_attr(): def test_attr():
x = tvm.Var('x') x = tvm.var('x')
y = tvm.Var('y') y = tvm.var('y')
stmt = tvm.make.AttrStmt( stmt = tvm.make.AttrStmt(
y, "stride", 10, tvm.make.Evaluate(x + 1)); y, "stride", 10, tvm.make.Evaluate(x + 1));
assert stmt.node == y assert stmt.node == y
...@@ -52,15 +52,15 @@ def test_attr(): ...@@ -52,15 +52,15 @@ def test_attr():
def test_basic(): def test_basic():
a = tvm.Var('a') a = tvm.var('a')
b = tvm.Var('b') b = tvm.var('b')
c = a + b c = a + b
assert str(c) == '(%s + %s)' % (a.name, b.name) assert str(c) == '(%s + %s)' % (a.name, b.name)
def test_stmt(): def test_stmt():
x = tvm.make.Evaluate(0) x = tvm.make.Evaluate(0)
tvm.make.For(tvm.Var('i'), 0, 1, tvm.make.For(tvm.var('i'), 0, 1,
tvm.stmt.For.Serial, 0, tvm.stmt.For.Serial, 0,
x) x)
......
tests/python/unittest/test_lang_buffer.py
import tvm import tvm
def test_buffer(): def test_buffer():
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
l = tvm.Var('l') l = tvm.var('l')
Ab = tvm.Buffer((m, n), tvm.float32) Ab = tvm.decl_buffer((m, n), tvm.float32)
Bb = tvm.Buffer((n, l), tvm.float32) Bb = tvm.decl_buffer((n, l), tvm.float32)
assert isinstance(Ab, tvm.schedule.Buffer) assert isinstance(Ab, tvm.schedule.Buffer)
assert Ab.dtype == tvm.float32 assert Ab.dtype == tvm.float32
......
tests/python/unittest/test_lang_container.py
...@@ -11,8 +11,8 @@ def test_array_save_load_json(): ...@@ -11,8 +11,8 @@ def test_array_save_load_json():
assert(a[1].value == 2) assert(a[1].value == 2)
def test_map(): def test_map():
a = tvm.Var('a') a = tvm.var('a')
b = tvm.Var('b') b = tvm.var('b')
amap = tvm.convert({a: 2, amap = tvm.convert({a: 2,
b: 3}) b: 3})
assert a in amap assert a in amap
...@@ -22,8 +22,8 @@ def test_map(): ...@@ -22,8 +22,8 @@ def test_map():
assert a + 1 not in amap assert a + 1 not in amap
def test_map_save_load_json(): def test_map_save_load_json():
a = tvm.Var('a') a = tvm.var('a')
b = tvm.Var('b') b = tvm.var('b')
amap = tvm.convert({a: 2, amap = tvm.convert({a: 2,
b: 3}) b: 3})
json_str = tvm.save_json(amap) json_str = tvm.save_json(amap)
......
tests/python/unittest/test_lang_group.py
...@@ -2,8 +2,8 @@ ...@@ -2,8 +2,8 @@
import tvm import tvm
def test_scan_group(): def test_scan_group():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x") x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x")
s_state = tvm.placeholder((m, n)) s_state = tvm.placeholder((m, n))
s_init = tvm.compute((1, n), lambda _, i: x[0, i]) s_init = tvm.compute((1, n), lambda _, i: x[0, i])
...@@ -13,7 +13,7 @@ def test_scan_group(): ...@@ -13,7 +13,7 @@ def test_scan_group():
s_update3 = tvm.compute((m, n), lambda t, i: s_update2[t, i] + 1) s_update3 = tvm.compute((m, n), lambda t, i: s_update2[t, i] + 1)
res = tvm.scan(s_init, s_update3, s_state, inputs=x) res = tvm.scan(s_init, s_update3, s_state, inputs=x)
s = tvm.Schedule(res.op) s = tvm.create_schedule(res.op)
assert s[s_update1].group is not None assert s[s_update1].group is not None
assert s[s_update2].group == s[s_update1].group assert s[s_update2].group == s[s_update1].group
# Assign within group, is valid # Assign within group, is valid
...@@ -34,12 +34,12 @@ def test_scan_group(): ...@@ -34,12 +34,12 @@ def test_scan_group():
pass pass
def test_compute_group(): def test_compute_group():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x") x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x")
x1 = tvm.compute(x.shape, lambda *i: x(*i) + 1, name="x1") x1 = tvm.compute(x.shape, lambda *i: x(*i) + 1, name="x1")
x2 = tvm.compute(x.shape, lambda *i: x1(*i) + 2, name="x2") x2 = tvm.compute(x.shape, lambda *i: x1(*i) + 2, name="x2")
s = tvm.Schedule(x2.op) s = tvm.create_schedule(x2.op)
g = s.create_group(outputs=x1, inputs=x, include_inputs=True) g = s.create_group(outputs=x1, inputs=x, include_inputs=True)
assert s[x1].group == g assert s[x1].group == g
assert s[x].group == g assert s[x].group == g
...@@ -48,12 +48,12 @@ def test_compute_group(): ...@@ -48,12 +48,12 @@ def test_compute_group():
assert g.num_child_stages == 2 assert g.num_child_stages == 2
def test_nest_group(): def test_nest_group():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x") x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x")
x1 = tvm.compute(x.shape, lambda *i: x(*i) + 1, name="x1") x1 = tvm.compute(x.shape, lambda *i: x(*i) + 1, name="x1")
x2 = tvm.compute(x.shape, lambda *i: x1(*i) + 2, name="x2") x2 = tvm.compute(x.shape, lambda *i: x1(*i) + 2, name="x2")
s = tvm.Schedule(x2.op) s = tvm.create_schedule(x2.op)
g1 = s.create_group(outputs=x1, inputs=x) g1 = s.create_group(outputs=x1, inputs=x)
g2 = s.create_group(outputs=x1, inputs=x, include_inputs=True) g2 = s.create_group(outputs=x1, inputs=x, include_inputs=True)
assert set(s.groups) == set([g1, g2]) assert set(s.groups) == set([g1, g2])
......
tests/python/unittest/test_lang_schedule.py
...@@ -2,14 +2,14 @@ import tvm ...@@ -2,14 +2,14 @@ import tvm
import pickle as pkl import pickle as pkl
def test_schedule_create(): def test_schedule_create():
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
B = tvm.placeholder((n, l), name='B') B = tvm.placeholder((n, l), name='B')
AA = tvm.compute((m, l), lambda i, j: A[i, j]) AA = tvm.compute((m, l), lambda i, j: A[i, j])
T = tvm.compute((m, n, l), lambda i, j, k: AA(i, k) * B(j, k)) T = tvm.compute((m, n, l), lambda i, j, k: AA(i, k) * B(j, k))
s = tvm.Schedule(T.op) s = tvm.create_schedule(T.op)
s[AA].set_scope("shared") s[AA].set_scope("shared")
xo, xi = s[T].split(T.op.axis[0], factor=10) xo, xi = s[T].split(T.op.axis[0], factor=10)
xi1, xi2 = s[T].split(xi, factor=2) xi1, xi2 = s[T].split(xi, factor=2)
...@@ -31,11 +31,11 @@ def test_schedule_create(): ...@@ -31,11 +31,11 @@ def test_schedule_create():
assert(str(s_loaded.outputs[0].body) == str(s.outputs[0].body)) assert(str(s_loaded.outputs[0].body) == str(s.outputs[0].body))
def test_reorder(): def test_reorder():
m = tvm.Var('m') m = tvm.var('m')
A = tvm.placeholder((m,), name='A') A = tvm.placeholder((m,), name='A')
T = tvm.compute(m, lambda i: A[i+1]) T = tvm.compute(m, lambda i: A[i+1])
s = tvm.Schedule(T.op) s = tvm.create_schedule(T.op)
xo, xi = s[T].split(T.op.axis[0], factor=10) xo, xi = s[T].split(T.op.axis[0], factor=10)
xi1, xi2 = s[T].split(xi, factor=2) xi1, xi2 = s[T].split(xi, factor=2)
order = (xi2, xi1, xo) order = (xi2, xi1, xo)
...@@ -44,45 +44,45 @@ def test_reorder(): ...@@ -44,45 +44,45 @@ def test_reorder():
assert tuple(s[T].leaf_iter_vars) == order assert tuple(s[T].leaf_iter_vars) == order
def test_split(): def test_split():
m = tvm.Var('m') m = tvm.var('m')
A = tvm.placeholder((m,), name='A') A = tvm.placeholder((m,), name='A')
T = tvm.compute((m,), lambda i: A[i]) T = tvm.compute((m,), lambda i: A[i])
s = tvm.Schedule(T.op) s = tvm.create_schedule(T.op)
xo, xi = s[T].split(T.op.axis[0], factor=10) xo, xi = s[T].split(T.op.axis[0], factor=10)
assert tuple(s[T].leaf_iter_vars) == (xo, xi) assert tuple(s[T].leaf_iter_vars) == (xo, xi)
def test_tile(): def test_tile():
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((m, n), name='A') A = tvm.placeholder((m, n), name='A')
T = tvm.compute((m, n), lambda i, j: A[i, j]) T = tvm.compute((m, n), lambda i, j: A[i, j])
s = tvm.Schedule(T.op) s = tvm.create_schedule(T.op)
xo, yo, xi, yi = s[T].tile(T.op.axis[0], T.op.axis[1], x_factor=10, y_factor=5) xo, yo, xi, yi = s[T].tile(T.op.axis[0], T.op.axis[1], x_factor=10, y_factor=5)
assert tuple(s[T].leaf_iter_vars) == (xo, yo, xi, yi) assert tuple(s[T].leaf_iter_vars) == (xo, yo, xi, yi)
def test_fuse(): def test_fuse():
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((m, n), name='A') A = tvm.placeholder((m, n), name='A')
T = tvm.compute((m, n), lambda i, j: A[i, j]) T = tvm.compute((m, n), lambda i, j: A[i, j])
s = tvm.Schedule(T.op) s = tvm.create_schedule(T.op)
xo, yo, xi, yi = s[T].tile(T.op.axis[0], T.op.axis[1], x_factor=10, y_factor=5) xo, yo, xi, yi = s[T].tile(T.op.axis[0], T.op.axis[1], x_factor=10, y_factor=5)
fused = s[T].fuse(yo, xo) fused = s[T].fuse(yo, xo)
assert any(isinstance(x, tvm.schedule.Fuse) for x in s[T].relations) assert any(isinstance(x, tvm.schedule.Fuse) for x in s[T].relations)
assert tuple(s[T].leaf_iter_vars) == (fused, xi, yi) assert tuple(s[T].leaf_iter_vars) == (fused, xi, yi)
def test_vectorize(): def test_vectorize():
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((m, n), name='A') A = tvm.placeholder((m, n), name='A')
T = tvm.compute((m, n), lambda i, j: A[i, j]) T = tvm.compute((m, n), lambda i, j: A[i, j])
s = tvm.Schedule(T.op) s = tvm.create_schedule(T.op)
xo, yo, xi, yi = s[T].tile(T.op.axis[0], T.op.axis[1], x_factor=10, y_factor=5) 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].vectorize(yi)
s[T].unroll(xi) s[T].unroll(xi)
...@@ -92,20 +92,20 @@ def test_vectorize(): ...@@ -92,20 +92,20 @@ def test_vectorize():
assert s[T].iter_var_attrs[yi].iter_type == VECTORIZE assert s[T].iter_var_attrs[yi].iter_type == VECTORIZE
def test_rfactor(): def test_rfactor():
n = tvm.Var('n') n = tvm.var('n')
k1 = tvm.reduce_axis((0, n), name="k1") k1 = tvm.reduce_axis((0, n), name="k1")
k2 = tvm.reduce_axis((0, n), name="k2") k2 = tvm.reduce_axis((0, n), name="k2")
A = tvm.placeholder((n, n, n), name='A') A = tvm.placeholder((n, n, n), name='A')
B = tvm.compute((n, ), lambda i: tvm.sum(A[i, k1, k2], axis=[k1, k2])) B = tvm.compute((n, ), lambda i: tvm.sum(A[i, k1, k2], axis=[k1, k2]))
# normal schedule # normal schedule
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
BF = s.rfactor(B, k1) BF = s.rfactor(B, k1)
assert(tuple(BF.shape) == (n, n)) assert(tuple(BF.shape) == (n, n))
assert(set(BF.op.body.axis) == set([k2])) assert(set(BF.op.body.axis) == set([k2]))
assert(s[B].op.body.axis[0].dom.extent == n) assert(s[B].op.body.axis[0].dom.extent == n)
assert(len(s[B].all_iter_vars) == 2) assert(len(s[B].all_iter_vars) == 2)
# schedule with splot # schedule with splot
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
ko, ki = s[B].split(k1, factor=4) ko, ki = s[B].split(k1, factor=4)
xo, xi = s[B].split(B.op.axis[0], factor=8) xo, xi = s[B].split(B.op.axis[0], factor=8)
BF = s.rfactor(B, ki) BF = s.rfactor(B, ki)
......
tests/python/unittest/test_lang_tensor.py
import tvm import tvm
def test_tensor(): def test_tensor():
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
B = tvm.placeholder((n, l), name='B') B = tvm.placeholder((n, l), name='B')
T = tvm.compute((m, n, l), lambda i, j, k: A[i, k] * B[j, k]) T = tvm.compute((m, n, l), lambda i, j, k: A[i, k] * B[j, k])
...@@ -19,7 +19,7 @@ def test_tensor(): ...@@ -19,7 +19,7 @@ def test_tensor():
def test_conv1d(): def test_conv1d():
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((n+2), name='A') A = tvm.placeholder((n+2), name='A')
def computeB(ii): def computeB(ii):
i = ii + 1 i = ii + 1
...@@ -28,15 +28,15 @@ def test_conv1d(): ...@@ -28,15 +28,15 @@ def test_conv1d():
def test_tensor_slice(): def test_tensor_slice():
n = tvm.Var('n') n = tvm.var('n')
A = tvm.compute((n, n), lambda i, j: 1) A = tvm.compute((n, n), lambda i, j: 1)
B = tvm.compute((n,), lambda i: A[0][i] + A[0][i]) B = tvm.compute((n,), lambda i: A[0][i] + A[0][i])
def test_tensor_reduce(): def test_tensor_reduce():
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
B = tvm.placeholder((n, l), name='B') B = tvm.placeholder((n, l), name='B')
T = tvm.compute((m, n, l), lambda i, j, k: A[i, k] * B[j, k]) T = tvm.compute((m, n, l), lambda i, j, k: A[i, k] * B[j, k])
...@@ -50,8 +50,8 @@ def test_tensor_reduce(): ...@@ -50,8 +50,8 @@ def test_tensor_reduce():
def test_tensor_scan(): def test_tensor_scan():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
x = tvm.placeholder((m, n)) x = tvm.placeholder((m, n))
s = tvm.placeholder((m, n)) s = tvm.placeholder((m, n))
res = tvm.scan(tvm.compute((1, n), lambda _, i: x[0, i]), res = tvm.scan(tvm.compute((1, n), lambda _, i: x[0, i]),
...@@ -60,8 +60,8 @@ def test_tensor_scan(): ...@@ -60,8 +60,8 @@ def test_tensor_scan():
assert tuple(res.shape) == (m, n) assert tuple(res.shape) == (m, n)
def test_scan_multi_out(): def test_scan_multi_out():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
x1 = tvm.placeholder((m, n)) x1 = tvm.placeholder((m, n))
s1 = tvm.placeholder((m, n)) s1 = tvm.placeholder((m, n))
x2 = tvm.placeholder((m, n)) x2 = tvm.placeholder((m, n))
...@@ -81,7 +81,7 @@ def test_scan_multi_out(): ...@@ -81,7 +81,7 @@ def test_scan_multi_out():
assert isinstance(zz, tvm.tensor.ScanOp) assert isinstance(zz, tvm.tensor.ScanOp)
def test_extern(): def test_extern():
m = tvm.Var('m') m = tvm.var('m')
A = tvm.placeholder((m,), name='A') A = tvm.placeholder((m,), name='A')
def extern_func(ins, outs): def extern_func(ins, outs):
...@@ -92,7 +92,7 @@ def test_extern(): ...@@ -92,7 +92,7 @@ def test_extern():
def test_extern_multi_out(): def test_extern_multi_out():
m = tvm.Var('m') m = tvm.var('m')
A = tvm.placeholder((m,), name='A') A = tvm.placeholder((m,), name='A')
B = tvm.compute((m,), lambda i: A[i] * 10) B = tvm.compute((m,), lambda i: A[i] * 10)
......
tests/python/unittest/test_module_load.py
...@@ -26,9 +26,9 @@ def test_dso_module_load(): ...@@ -26,9 +26,9 @@ def test_dso_module_load():
temp = testing.tempdir() temp = testing.tempdir()
def save_object(names): def save_object(names):
n = tvm.Var('n') n = tvm.var('n')
Ab = tvm.Buffer((n, ), dtype) Ab = tvm.decl_buffer((n, ), dtype)
i = tvm.Var('i') i = tvm.var('i')
# for i in 0 to n-1: # for i in 0 to n-1:
stmt = tvm.make.For( stmt = tvm.make.For(
i, 0, n - 1, 0, 0, i, 0, n - 1, 0, 0,
......
tests/python/unittest/test_pass_basic.py
import tvm import tvm
def test_simplify(): def test_simplify():
x = tvm.Var('x') x = tvm.var('x')
e1 = tvm.ir_pass.Simplify(x + 2 + 1) e1 = tvm.ir_pass.Simplify(x + 2 + 1)
assert(tvm.ir_pass.Equal(e1, x + 3)) assert(tvm.ir_pass.Equal(e1, x + 3))
e2 = tvm.ir_pass.Simplify(x * 3 + 5 * x) e2 = tvm.ir_pass.Simplify(x * 3 + 5 * x)
...@@ -14,15 +14,15 @@ def test_simplify(): ...@@ -14,15 +14,15 @@ def test_simplify():
def test_verify_ssa(): def test_verify_ssa():
x = tvm.Var('x') x = tvm.var('x')
y = tvm.Var() y = tvm.var()
z = tvm.make.Evaluate(x + y) z = tvm.make.Evaluate(x + y)
assert(tvm.ir_pass.VerifySSA(z)) assert(tvm.ir_pass.VerifySSA(z))
def test_convert_ssa(): def test_convert_ssa():
x = tvm.Var('x') x = tvm.var('x')
y = tvm.Var() y = tvm.var()
let1 = tvm.make.Let(x, 1, x + 1) let1 = tvm.make.Let(x, 1, x + 1)
let2 = tvm.make.Let(x, 1, x + y) let2 = tvm.make.Let(x, 1, x + y)
z = tvm.make.Evaluate(let1 + let2) z = tvm.make.Evaluate(let1 + let2)
...@@ -32,7 +32,7 @@ def test_convert_ssa(): ...@@ -32,7 +32,7 @@ def test_convert_ssa():
def test_expr_use_var(): def test_expr_use_var():
x = tvm.Var('x') x = tvm.var('x')
assert(tvm.ir_pass.ExprUseVar(x+1, x)) assert(tvm.ir_pass.ExprUseVar(x+1, x))
assert(not tvm.ir_pass.ExprUseVar(1+10, x)) assert(not tvm.ir_pass.ExprUseVar(1+10, x))
......
tests/python/unittest/test_pass_inline.py
import tvm import tvm
def test_inline(): def test_inline():
m = tvm.Var('m') m = tvm.var('m')
A = tvm.placeholder((m,), name='A') A = tvm.placeholder((m,), name='A')
T = tvm.compute((m,), lambda i,: A[i] + 10, name='T') T = tvm.compute((m,), lambda i,: A[i] + 10, name='T')
stmt = tvm.make.Evaluate(T[10] + 11 * T[100]) stmt = tvm.make.Evaluate(T[10] + 11 * T[100])
......
tests/python/unittest/test_pass_loop_partition.py
import tvm import tvm
def test_basic(): def test_basic():
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((n, ), name='A') A = tvm.placeholder((n, ), name='A')
B = tvm.placeholder((n, ), name='B') B = tvm.placeholder((n, ), name='B')
T = tvm.compute((n, ), lambda i: A[i]+B[i]) T = tvm.compute((n, ), lambda i: A[i]+B[i])
s = tvm.Schedule(T.op) s = tvm.create_schedule(T.op)
xo, xi = s[T].split(T.op.axis[0], factor=4) xo, xi = s[T].split(T.op.axis[0], factor=4)
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
...@@ -16,11 +16,11 @@ def test_basic(): ...@@ -16,11 +16,11 @@ def test_basic():
print(stmt) print(stmt)
def test_multi_loop(): def test_multi_loop():
i = tvm.Var('i') i = tvm.var('i')
j = tvm.Var('j') j = tvm.var('j')
k = tvm.Var('k') k = tvm.var('k')
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
stmt = tvm.make.For( stmt = tvm.make.For(
i, 0, 4, 0, 0, i, 0, 4, 0, 0,
tvm.make.For( tvm.make.For(
...@@ -34,11 +34,11 @@ def test_multi_loop(): ...@@ -34,11 +34,11 @@ def test_multi_loop():
print(stmt) print(stmt)
def test_multi_if(): def test_multi_if():
i = tvm.Var('i') i = tvm.var('i')
j = tvm.Var('j') j = tvm.var('j')
k = tvm.Var('k') k = tvm.var('k')
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
stmt = tvm.make.For( stmt = tvm.make.For(
i, 0, 4, 0, 0, i, 0, 4, 0, 0,
tvm.make.For( tvm.make.For(
...@@ -54,12 +54,12 @@ def test_multi_if(): ...@@ -54,12 +54,12 @@ def test_multi_if():
print(stmt) print(stmt)
def test_thread_axis(): def test_thread_axis():
m = tvm.Var('m') m = tvm.var('m')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
B = tvm.compute((m, l), lambda i, j: A[i, j] + 3, name='B') B = tvm.compute((m, l), lambda i, j: A[i, j] + 3, name='B')
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
s[B].set_scope("shared") s[B].set_scope("shared")
num_thread = 16 num_thread = 16
......
tests/python/unittest/test_pass_makeapi.py
...@@ -3,18 +3,18 @@ import numpy ...@@ -3,18 +3,18 @@ import numpy
def test_makeapi(): def test_makeapi():
"""Not yet working, mock design""" """Not yet working, mock design"""
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((n,), name='A') A = tvm.placeholder((n,), name='A')
B = tvm.placeholder((n,), name='B') B = tvm.placeholder((n,), name='B')
C = tvm.compute(A.shape, lambda *i: A(*i) + B(*i), name='C') C = tvm.compute(A.shape, lambda *i: A(*i) + B(*i), name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
stmt = tvm.schedule.ScheduleOps(s, bounds) stmt = tvm.schedule.ScheduleOps(s, bounds)
Ab = tvm.Buffer(A.shape, A.dtype, name='A') Ab = tvm.decl_buffer(A.shape, A.dtype, name='A')
Bb = tvm.Buffer(B.shape, B.dtype, name='B') Bb = tvm.decl_buffer(B.shape, B.dtype, name='B')
Cb = tvm.Buffer(C.shape, C.dtype, name='C') Cb = tvm.decl_buffer(C.shape, C.dtype, name='C')
stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, B:Bb, C:Cb}) stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, B:Bb, C:Cb})
num_unpacked_args = 2 num_unpacked_args = 2
......
tests/python/unittest/test_pass_remove_no_op.py
import tvm import tvm
def test_remove_no_op(): def test_remove_no_op():
i = tvm.Var('i') i = tvm.var('i')
j = tvm.Var('j') j = tvm.var('j')
k = tvm.Var('k') k = tvm.var('k')
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
dtype = 'int64' dtype = 'int64'
Ab = tvm.Buffer((n, ), dtype) Ab = tvm.decl_buffer((n, ), dtype)
stmt = tvm.make.For( stmt = tvm.make.For(
i, 0, 4, 0, 0, i, 0, 4, 0, 0,
tvm.make.For( tvm.make.For(
......
tests/python/unittest/test_pass_simplify.py
...@@ -4,10 +4,10 @@ import numpy ...@@ -4,10 +4,10 @@ import numpy
def test_simplify(): def test_simplify():
"""Not yet working, mock design""" """Not yet working, mock design"""
dtype = 'int64' dtype = 'int64'
n = tvm.Var('n') n = tvm.var('n')
Ab = tvm.Buffer((n, ), dtype) Ab = tvm.decl_buffer((n, ), dtype)
i = tvm.Var('i') i = tvm.var('i')
j = tvm.Var('j') j = tvm.var('j')
# for i in 0 to n-1: # for i in 0 to n-1:
stmt = tvm.make.For( stmt = tvm.make.For(
i, 2, n, 0, 0, i, 2, n, 0, 0,
...@@ -22,7 +22,7 @@ def test_simplify(): ...@@ -22,7 +22,7 @@ def test_simplify():
def test_basic(): def test_basic():
m = tvm.Var('m') m = tvm.var('m')
ret = tvm.ir_pass.CanonicalSimplify(tvm.make.Evaluate(m-1)) ret = tvm.ir_pass.CanonicalSimplify(tvm.make.Evaluate(m-1))
assert str(ret.value) == "(m - 1)" assert str(ret.value) == "(m - 1)"
......
tests/python/unittest/test_pass_split_pipeline.py
...@@ -6,7 +6,7 @@ def lower(s, args): ...@@ -6,7 +6,7 @@ def lower(s, args):
for x in args: for x in args:
assert isinstance(x, tvm.tensor.Tensor) assert isinstance(x, tvm.tensor.Tensor)
buf = tvm.Buffer(x.shape, dtype=x.dtype, name=x.op.name) buf = tvm.decl_buffer(x.shape, dtype=x.dtype, name=x.op.name)
binds[x] = buf binds[x] = buf
arg_list.append(buf) arg_list.append(buf)
s.normalize() s.normalize()
...@@ -28,7 +28,7 @@ def test_basic_pipeline(): ...@@ -28,7 +28,7 @@ def test_basic_pipeline():
stages.append(B) stages.append(B)
B = tvm.compute((n,), lambda i: B[i] + k, name="A%s" % k) B = tvm.compute((n,), lambda i: B[i] + k, name="A%s" % k)
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], nparts=1) xo, xi = s[B].split(B.op.axis[0], nparts=1)
s[B].bind(xo, tvm.thread_axis("pipeline")) s[B].bind(xo, tvm.thread_axis("pipeline"))
xo, xi = s[B].split(xi, factor=4) xo, xi = s[B].split(xi, factor=4)
...@@ -43,13 +43,13 @@ def test_basic_pipeline(): ...@@ -43,13 +43,13 @@ def test_basic_pipeline():
assert(tvm.ir_pass.VerifySSA(stmt)) assert(tvm.ir_pass.VerifySSA(stmt))
def test_conv1d(): def test_conv1d():
n = tvm.Var('n') n = tvm.var('n')
A = tvm.compute((n+2), lambda i: 1, name='A') A = tvm.compute((n+2), lambda i: 1, name='A')
def computeB(ii): def computeB(ii):
i = ii + 1 i = ii + 1
return A[i-1] + A[i] + A[i+1] return A[i-1] + A[i] + A[i+1]
B = tvm.compute(n, computeB, name='B') B = tvm.compute(n, computeB, name='B')
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
px, xi = s[B].split(B.op.axis[0], nparts=1) px, xi = s[B].split(B.op.axis[0], nparts=1)
s[B].bind(px, tvm.thread_axis("pipeline")) s[B].bind(px, tvm.thread_axis("pipeline"))
s[A].compute_at(s[B], px) s[A].compute_at(s[B], px)
......
tests/python/unittest/test_pass_storage_flatten.py
import tvm import tvm
def test_flatten2(): def test_flatten2():
m = tvm.Var('m') m = tvm.var('m')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1') A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1')
A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2') A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2')
s = tvm.Schedule(A2.op) s = tvm.create_schedule(A2.op)
xo, xi = s[A2].split(A2.op.axis[0], 8) xo, xi = s[A2].split(A2.op.axis[0], 8)
s[A1].compute_at(s[A2], xo) s[A1].compute_at(s[A2], xo)
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
...@@ -15,8 +15,8 @@ def test_flatten2(): ...@@ -15,8 +15,8 @@ def test_flatten2():
stmt = tvm.schedule.ScheduleOps(s, bounds) stmt = tvm.schedule.ScheduleOps(s, bounds)
print(stmt) print(stmt)
Ab = tvm.Buffer(A.shape, A.dtype, name='A') Ab = tvm.decl_buffer(A.shape, A.dtype, name='A')
A2b = tvm.Buffer(A2.shape, A2.dtype, name='A2') A2b = tvm.decl_buffer(A2.shape, A2.dtype, name='A2')
stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, A2: A2b}) stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, A2: A2b})
stmt = tvm.ir_pass.Simplify(stmt) stmt = tvm.ir_pass.Simplify(stmt)
print(stmt) print(stmt)
......
tests/python/unittest/test_pass_storage_sync.py
import tvm import tvm
def test_storage_sync(): def test_storage_sync():
m = tvm.Var('m') m = tvm.var('m')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1') A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1')
A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2') A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2')
s = tvm.Schedule(A2.op) s = tvm.create_schedule(A2.op)
xo, xi = s[A2].split(A2.op.axis[0], factor=8) xo, xi = s[A2].split(A2.op.axis[0], factor=8)
s[A2].bind(xo, tvm.thread_axis("blockIdx.x")) s[A2].bind(xo, tvm.thread_axis("blockIdx.x"))
s[A1].compute_at(s[A2], xo) s[A1].compute_at(s[A2], xo)
...@@ -18,8 +18,8 @@ def test_storage_sync(): ...@@ -18,8 +18,8 @@ def test_storage_sync():
assert isinstance(bounds, tvm.collections.Map) assert isinstance(bounds, tvm.collections.Map)
stmt = tvm.schedule.ScheduleOps(s, bounds) stmt = tvm.schedule.ScheduleOps(s, bounds)
print(stmt) print(stmt)
Ab = tvm.Buffer(A.shape, A.dtype, name='A') Ab = tvm.decl_buffer(A.shape, A.dtype, name='A')
A2b = tvm.Buffer(A2.shape, A2.dtype, name='A2') A2b = tvm.decl_buffer(A2.shape, A2.dtype, name='A2')
stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, A2: A2b}) stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, A2: A2b})
f = tvm.ir_pass.MakeAPI(stmt, "test", [Ab, A2b], 0) f = tvm.ir_pass.MakeAPI(stmt, "test", [Ab, A2b], 0)
flist = tvm.ir_pass.SplitHostDevice(f) flist = tvm.ir_pass.SplitHostDevice(f)
......
tests/python/unittest/test_pass_unroll.py
...@@ -2,10 +2,10 @@ import tvm ...@@ -2,10 +2,10 @@ import tvm
def test_unroll_loop(): def test_unroll_loop():
dtype = 'int64' dtype = 'int64'
n = tvm.Var('n') n = tvm.var('n')
Ab = tvm.Buffer((n, ), dtype) Ab = tvm.decl_buffer((n, ), dtype)
i = tvm.Var('i') i = tvm.var('i')
j = tvm.Var('j') j = tvm.var('j')
# for i in 0 to n-1: # for i in 0 to n-1:
stmt = tvm.make.For( stmt = tvm.make.For(
i, n, 2, 0, 0, i, n, 2, 0, 0,
......
tests/python/unittest/test_pass_vectorize.py
...@@ -2,10 +2,10 @@ import tvm ...@@ -2,10 +2,10 @@ import tvm
def test_vectorize_loop(): def test_vectorize_loop():
dtype = 'int64' dtype = 'int64'
n = tvm.Var('n') n = tvm.var('n')
Ab = tvm.Buffer((n, ), dtype) Ab = tvm.decl_buffer((n, ), dtype)
i = tvm.Var('i') i = tvm.var('i')
j = tvm.Var('j') j = tvm.var('j')
VECTORIZE = 2 VECTORIZE = 2
# for i in 0 to n-1: # for i in 0 to n-1:
stmt = tvm.make.For( stmt = tvm.make.For(
......
tests/python/unittest/test_pass_virtual_thread.py
import tvm import tvm
def test_virtual_thread(): def test_virtual_thread():
m = tvm.Var('m') m = tvm.var('m')
A = tvm.placeholder((m, ), name='A') A = tvm.placeholder((m, ), name='A')
A1 = tvm.compute((m,), lambda i: A[i], name='A1') A1 = tvm.compute((m,), lambda i: A[i], name='A1')
A2 = tvm.compute((m,), lambda i: A1[i] + 3, name='A2') A2 = tvm.compute((m,), lambda i: A1[i] + 3, name='A2')
s = tvm.Schedule(A2.op) s = tvm.create_schedule(A2.op)
vx = tvm.thread_axis("vthread", name="vx") vx = tvm.thread_axis("vthread", name="vx")
xo, xi = s[A2].split(A2.op.axis[0], nparts=2) xo, xi = s[A2].split(A2.op.axis[0], nparts=2)
s[A2].bind(xo, vx) s[A2].bind(xo, vx)
...@@ -17,8 +17,8 @@ def test_virtual_thread(): ...@@ -17,8 +17,8 @@ def test_virtual_thread():
assert isinstance(bounds, tvm.collections.Map) assert isinstance(bounds, tvm.collections.Map)
stmt = tvm.schedule.ScheduleOps(s, bounds) stmt = tvm.schedule.ScheduleOps(s, bounds)
Ab = tvm.Buffer(A.shape, A.dtype, name='A') Ab = tvm.decl_buffer(A.shape, A.dtype, name='A')
A2b = tvm.Buffer(A2.shape, A2.dtype, name='A2') A2b = tvm.decl_buffer(A2.shape, A2.dtype, name='A2')
stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, A2: A2b}) stmt = tvm.ir_pass.StorageFlatten(stmt, {A: Ab, A2: A2b})
stmt = tvm.ir_pass.Simplify(stmt) stmt = tvm.ir_pass.Simplify(stmt)
stmt = tvm.ir_pass.InjectVirtualThread(stmt) stmt = tvm.ir_pass.InjectVirtualThread(stmt)
......
tests/python/unittest/test_runtime_packed_func.py
...@@ -10,7 +10,7 @@ def test_get_global(): ...@@ -10,7 +10,7 @@ def test_get_global():
return 10 return 10
# get it out from global function table # get it out from global function table
f = tvm.get_global_func("my_packed_func") f = tvm.get_global_func("my_packed_func")
assert isinstance(f, tvm.nd.Function) assert isinstance(f, tvm.Function)
y = f(*targs) y = f(*targs)
assert y == 10 assert y == 10
...@@ -32,7 +32,7 @@ def test_convert(): ...@@ -32,7 +32,7 @@ def test_convert():
assert(tuple(args) == targs) assert(tuple(args) == targs)
f = tvm.convert(myfunc) f = tvm.convert(myfunc)
assert isinstance(f, tvm.nd.Function) assert isinstance(f, tvm.Function)
f(*targs) f(*targs)
def test_byte_array(): def test_byte_array():
......
tests/python/unittest/test_schedule_bound_inference.py
import tvm import tvm
def test_bound1(): def test_bound1():
m = tvm.Var('m') m = tvm.var('m')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1') A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1')
A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2') A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2')
s = tvm.Schedule([A2.op]) s = tvm.create_schedule([A2.op])
xo, xi = s[A2].split(s[A2].op.axis[0], 8) xo, xi = s[A2].split(s[A2].op.axis[0], 8)
s[A1].compute_at(s[A2], xo) s[A1].compute_at(s[A2], xo)
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
...@@ -15,12 +15,12 @@ def test_bound1(): ...@@ -15,12 +15,12 @@ def test_bound1():
assert(bounds[A1.op.axis[0]].extent.value == 8) assert(bounds[A1.op.axis[0]].extent.value == 8)
def test_bound2(): def test_bound2():
m = tvm.Var('m') m = tvm.var('m')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1') A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1')
A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2') A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2')
s = tvm.Schedule(A2.op) s = tvm.create_schedule(A2.op)
xo, yo, xi, yi = s[A2].tile(A2.op.axis[0], A2.op.axis[1], 8, 8) xo, yo, xi, yi = s[A2].tile(A2.op.axis[0], A2.op.axis[1], 8, 8)
s[A1].compute_at(s[A2], yo) s[A1].compute_at(s[A2], yo)
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
...@@ -29,13 +29,13 @@ def test_bound2(): ...@@ -29,13 +29,13 @@ def test_bound2():
assert(bounds[A1.op.axis[1]].extent.value == 8) assert(bounds[A1.op.axis[1]].extent.value == 8)
def test_bound3(): def test_bound3():
m = tvm.Var('m') m = tvm.var('m')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1') A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1')
A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2') A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2')
s = tvm.Schedule(A2.op) s = tvm.create_schedule(A2.op)
s[A1].set_scope("shared") s[A1].set_scope("shared")
xo, xi = s[A2].split(A2.op.axis[0], 32) xo, xi = s[A2].split(A2.op.axis[0], 32)
xi0, xi1 = s[A2].split(xi, nparts=16) xi0, xi1 = s[A2].split(xi, nparts=16)
...@@ -50,8 +50,8 @@ def test_bound3(): ...@@ -50,8 +50,8 @@ def test_bound3():
assert(bounds[A1.op.axis[1]].extent.value==16) assert(bounds[A1.op.axis[1]].extent.value==16)
def test_bound_scan(): def test_bound_scan():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
X = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x") X = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x")
s_state = tvm.placeholder((m, n)) s_state = tvm.placeholder((m, n))
s_init = tvm.compute((1, n), lambda _, i: X[0, i]) s_init = tvm.compute((1, n), lambda _, i: X[0, i])
...@@ -59,7 +59,7 @@ def test_bound_scan(): ...@@ -59,7 +59,7 @@ def test_bound_scan():
s_scan = tvm.scan(s_init, s_update, s_state) s_scan = tvm.scan(s_init, s_update, s_state)
assert tuple(s_scan.shape) == (m, n) assert tuple(s_scan.shape) == (m, n)
s = tvm.Schedule(s_scan.op) s = tvm.create_schedule(s_scan.op)
XX = s.cache_read(X, "local", s_update) XX = s.cache_read(X, "local", s_update)
xo, xi = s[s_update].split(s_update.op.axis[1], factor=4) xo, xi = s[s_update].split(s_update.op.axis[1], factor=4)
s[XX].compute_at(s[s_update], xo) s[XX].compute_at(s[s_update], xo)
...@@ -69,13 +69,13 @@ def test_bound_scan(): ...@@ -69,13 +69,13 @@ def test_bound_scan():
assert bounds[XX.op.axis[1]].extent.value == 4 assert bounds[XX.op.axis[1]].extent.value == 4
def test_bound_conv1d(): def test_bound_conv1d():
n = tvm.Var('n') n = tvm.var('n')
A = tvm.compute((n+2), lambda i: 1, name='A') A = tvm.compute((n+2), lambda i: 1, name='A')
def computeB(ii): def computeB(ii):
i = ii + 1 i = ii + 1
return A[i-1] + A[i] + A[i+1] return A[i-1] + A[i] + A[i+1]
B = tvm.compute(n, computeB, name='B') B = tvm.compute(n, computeB, name='B')
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
s[A].compute_at(s[B], B.op.axis[0]) s[A].compute_at(s[B], B.op.axis[0])
s.normalize() s.normalize()
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
...@@ -90,7 +90,7 @@ def test_bound_blur(): ...@@ -90,7 +90,7 @@ def test_bound_blur():
j = jj + 1 j = jj + 1
return A[i][j] + A[i-1][j] + A[i+1][j] + A[i][j+1] + A[i][j-1] return A[i][j] + A[i-1][j] + A[i+1][j] + A[i][j+1] + A[i][j-1]
B = tvm.compute((n-2, n-2), computeB, name='B') B = tvm.compute((n-2, n-2), computeB, name='B')
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
s[A].compute_at(s[B], B.op.axis[1]) s[A].compute_at(s[B], B.op.axis[1])
s.normalize() s.normalize()
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
...@@ -98,12 +98,12 @@ def test_bound_blur(): ...@@ -98,12 +98,12 @@ def test_bound_blur():
assert(bounds[A.op.axis[1]].extent.value == 3) assert(bounds[A.op.axis[1]].extent.value == 3)
def test_bound_rfactor(): def test_bound_rfactor():
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((n,), name='A') A = tvm.placeholder((n,), name='A')
k = tvm.reduce_axis((0, n)) k = tvm.reduce_axis((0, n))
B = tvm.compute((1,), lambda i: tvm.sum(A[k], axis=k, where=(i>1)), name='B') B = tvm.compute((1,), lambda i: tvm.sum(A[k], axis=k, where=(i>1)), name='B')
# schedule # schedule
s = tvm.Schedule(B.op) s = tvm.create_schedule(B.op)
kf, ki = s[B].split(k, nparts=4) kf, ki = s[B].split(k, nparts=4)
BF = s.rfactor(B, kf) BF = s.rfactor(B, kf)
s.normalize() s.normalize()
...@@ -113,12 +113,12 @@ def test_bound_rfactor(): ...@@ -113,12 +113,12 @@ def test_bound_rfactor():
assert(bounds[BF.op.axis[1]].extent.value == 1) assert(bounds[BF.op.axis[1]].extent.value == 1)
def test_bound_group_schedule(): def test_bound_group_schedule():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x") x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x")
x1 = tvm.compute(x.shape, lambda *i: x(*i) + 1, name="x1") x1 = tvm.compute(x.shape, lambda *i: x(*i) + 1, name="x1")
x2 = tvm.compute(x.shape, lambda *i: x1(*i) + 2, name="x2") x2 = tvm.compute(x.shape, lambda *i: x1(*i) + 2, name="x2")
s = tvm.Schedule(x2.op) s = tvm.create_schedule(x2.op)
g = s.create_group(outputs=x1, inputs=x, include_inputs=True) g = s.create_group(outputs=x1, inputs=x, include_inputs=True)
g.compute_at(s[x2], x2.op.axis[0]) g.compute_at(s[x2], x2.op.axis[0])
assert s[x1].group == g assert s[x1].group == g
...@@ -129,12 +129,12 @@ def test_bound_group_schedule(): ...@@ -129,12 +129,12 @@ def test_bound_group_schedule():
assert bounds[x.op.axis[1]].extent == n assert bounds[x.op.axis[1]].extent == n
def test_bound_nest_group(): def test_bound_nest_group():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x") x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x")
x1 = tvm.compute(x.shape, lambda *i: x(*i) + 1, name="x1") x1 = tvm.compute(x.shape, lambda *i: x(*i) + 1, name="x1")
x2 = tvm.compute(x.shape, lambda *i: x1(*i) + 2, name="x2") x2 = tvm.compute(x.shape, lambda *i: x1(*i) + 2, name="x2")
s = tvm.Schedule(x2.op) s = tvm.create_schedule(x2.op)
g1 = s.create_group(outputs=x, inputs=x, include_inputs=True) g1 = s.create_group(outputs=x, inputs=x, include_inputs=True)
g2 = s.create_group(outputs=x1, inputs=x, include_inputs=True) g2 = s.create_group(outputs=x1, inputs=x, include_inputs=True)
assert s[x].group == g1 assert s[x].group == g1
...@@ -150,13 +150,13 @@ def test_bound_nest_group(): ...@@ -150,13 +150,13 @@ def test_bound_nest_group():
def test_bound_nest_thread(): def test_bound_nest_thread():
m = tvm.Var('m') m = tvm.var('m')
A = tvm.placeholder((m), name='A') A = tvm.placeholder((m), name='A')
A1 = tvm.compute((m,), lambda i: A[i], name='A1') A1 = tvm.compute((m,), lambda i: A[i], name='A1')
A2 = tvm.compute((m,), lambda i: A1[i] + 2, name='A2') A2 = tvm.compute((m,), lambda i: A1[i] + 2, name='A2')
A3 = tvm.compute((m,), lambda i: A2[i] + 3, name='A3') A3 = tvm.compute((m,), lambda i: A2[i] + 3, name='A3')
s = tvm.Schedule(A3.op) s = tvm.create_schedule(A3.op)
s[A2].set_scope("shared") s[A2].set_scope("shared")
s[A1].set_scope("local") s[A1].set_scope("local")
...@@ -186,7 +186,7 @@ def test_gemm_bound(): ...@@ -186,7 +186,7 @@ def test_gemm_bound():
lambda ii, jj: tvm.sum(A[ii, k] * B[jj, k], axis=k), lambda ii, jj: tvm.sum(A[ii, k] * B[jj, k], axis=k),
name='CC') name='CC')
# schedule # schedule
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
xtile, ytile = 32, 32 xtile, ytile = 32, 32
scale = 8 scale = 8
num_thread = 8 num_thread = 8
......
tests/python/unittest/test_schedule_graph.py
import tvm import tvm
def test_scan(): def test_scan():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x") x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x")
s_state = tvm.placeholder((m, n)) s_state = tvm.placeholder((m, n))
s_init = tvm.compute((1, n), lambda _, i: x[0, i], name="s_init") s_init = tvm.compute((1, n), lambda _, i: x[0, i], name="s_init")
...@@ -16,7 +16,7 @@ def test_scan(): ...@@ -16,7 +16,7 @@ def test_scan():
assert set(body) == set([s_scan.op, s_update.op, s_up1.op]) assert set(body) == set([s_scan.op, s_update.op, s_up1.op])
def test_attach_path(): def test_attach_path():
s = tvm.Schedule(s_scan.op) s = tvm.create_schedule(s_scan.op)
s[x_trans].compute_at(s[s_update], s_update.op.axis[0]) s[x_trans].compute_at(s[s_update], s_update.op.axis[0])
apath = tvm.schedule.CreateAttachPath(s) apath = tvm.schedule.CreateAttachPath(s)
assert(tuple(apath[s_update.op]) == tuple([s_scan.op.scan_axis])) assert(tuple(apath[s_update.op]) == tuple([s_scan.op.scan_axis]))
...@@ -28,9 +28,9 @@ def test_scan(): ...@@ -28,9 +28,9 @@ def test_scan():
assert(fxpt[s_scan.spatial_axis_[0]].value != 0) assert(fxpt[s_scan.spatial_axis_[0]].value != 0)
def test_scan_fix_point(): def test_scan_fix_point():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
l = tvm.Var("l") l = tvm.var("l")
x = tvm.compute((l, m, n), lambda *i: tvm.const(1, "float32"), name="x") x = tvm.compute((l, m, n), lambda *i: tvm.const(1, "float32"), name="x")
s_state = tvm.placeholder((l, m, n)) s_state = tvm.placeholder((l, m, n))
s_init = tvm.compute((1, m, n), lambda _, i, j: x[0, i, j], name="s_init") s_init = tvm.compute((1, m, n), lambda _, i, j: x[0, i, j], name="s_init")
...@@ -74,8 +74,8 @@ def test_scan_fix_point(): ...@@ -74,8 +74,8 @@ def test_scan_fix_point():
assert(fxpt[s_scan.op.spatial_axis_[1]].value == 0) assert(fxpt[s_scan.op.spatial_axis_[1]].value == 0)
def test_scan5_multi_output(): def test_scan5_multi_output():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
x1 = tvm.placeholder((m, n)) x1 = tvm.placeholder((m, n))
s1 = tvm.placeholder((m, n)) s1 = tvm.placeholder((m, n))
x2 = tvm.placeholder((m, n)) x2 = tvm.placeholder((m, n))
...@@ -98,8 +98,8 @@ def test_scan_fix_point(): ...@@ -98,8 +98,8 @@ def test_scan_fix_point():
test_scan5_multi_output() test_scan5_multi_output()
def test_create_read_graph(): def test_create_read_graph():
m = tvm.Var('m') m = tvm.var('m')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
A1 = tvm.compute((m, l), lambda i, j: A[i, j]) A1 = tvm.compute((m, l), lambda i, j: A[i, j])
A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3) A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3)
......
tests/python/unittest/test_schedule_schedule_ops.py
...@@ -2,23 +2,23 @@ import tvm ...@@ -2,23 +2,23 @@ import tvm
def test_schedule0(): def test_schedule0():
m = tvm.Var('m') m = tvm.var('m')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1') A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1')
s = tvm.Schedule(A1.op) s = tvm.create_schedule(A1.op)
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
assert isinstance(bounds, tvm.collections.Map) assert isinstance(bounds, tvm.collections.Map)
stmt = tvm.schedule.ScheduleOps(s, bounds) stmt = tvm.schedule.ScheduleOps(s, bounds)
def test_schedule1(): def test_schedule1():
m = tvm.Var('m') m = tvm.var('m')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1') A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1')
s = tvm.Schedule(A1.op) s = tvm.create_schedule(A1.op)
xo, xi = s[A1].split(A1.op.axis[0], 8) xo, xi = s[A1].split(A1.op.axis[0], 8)
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
assert isinstance(bounds, tvm.collections.Map) assert isinstance(bounds, tvm.collections.Map)
...@@ -26,13 +26,13 @@ def test_schedule1(): ...@@ -26,13 +26,13 @@ def test_schedule1():
def test_schedule2(): def test_schedule2():
m = tvm.Var('m') m = tvm.var('m')
l = tvm.Var('l') l = tvm.var('l')
A = tvm.placeholder((m, l), name='A') A = tvm.placeholder((m, l), name='A')
A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1') A1 = tvm.compute((m, l), lambda i, j: A[i, j], name='A1')
A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2') A2 = tvm.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2')
s = tvm.Schedule(A2.op) s = tvm.create_schedule(A2.op)
xo, xi = s[A2].split(A2.op.axis[0], 8) xo, xi = s[A2].split(A2.op.axis[0], 8)
s[A1].compute_at(s[A2], xo) s[A1].compute_at(s[A2], xo)
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
...@@ -41,8 +41,8 @@ def test_schedule2(): ...@@ -41,8 +41,8 @@ def test_schedule2():
def test_schedule_scan(): def test_schedule_scan():
m = tvm.Var("m") m = tvm.var("m")
n = tvm.Var("n") n = tvm.var("n")
x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x") x = tvm.compute((m, n), lambda i, j: tvm.const(1, "float32"), name="x")
s_state = tvm.placeholder((m, n)) s_state = tvm.placeholder((m, n))
s_init = tvm.compute((1, n), lambda _, i: x[0, i]) s_init = tvm.compute((1, n), lambda _, i: x[0, i])
...@@ -50,7 +50,7 @@ def test_schedule_scan(): ...@@ -50,7 +50,7 @@ def test_schedule_scan():
res = tvm.scan(s_init, s_update, s_state) res = tvm.scan(s_init, s_update, s_state)
assert tuple(res.shape) == (m, n) assert tuple(res.shape) == (m, n)
s = tvm.Schedule(res.op) s = tvm.create_schedule(res.op)
s.normalize() s.normalize()
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
assert(bounds[res.op.scan_axis].min.value == 1) assert(bounds[res.op.scan_axis].min.value == 1)
...@@ -58,28 +58,28 @@ def test_schedule_scan(): ...@@ -58,28 +58,28 @@ def test_schedule_scan():
print(stmt) print(stmt)
def test_auto_inline(): def test_auto_inline():
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((m, n), name='A') A = tvm.placeholder((m, n), name='A')
B = tvm.placeholder((m, n), name='B') B = tvm.placeholder((m, n), name='B')
C = tvm.placeholder((m, n), name='C') C = tvm.placeholder((m, n), name='C')
T1 = tvm.compute((m, n), lambda i, j: A(i, j) * B(i, j), name='T1') T1 = tvm.compute((m, n), lambda i, j: A(i, j) * B(i, j), name='T1')
T2 = tvm.compute((m, n), lambda i, j: T1(i, j) + C(i, j), name='T2') T2 = tvm.compute((m, n), lambda i, j: T1(i, j) + C(i, j), name='T2')
s = tvm.Schedule(T2.op) s = tvm.create_schedule(T2.op)
tvm.schedule.AutoInlineElemWise(s) tvm.schedule.AutoInlineElemWise(s)
s.normalize() s.normalize()
bounds = tvm.schedule.InferBound(s) bounds = tvm.schedule.InferBound(s)
stmt = tvm.schedule.ScheduleOps(s, bounds) stmt = tvm.schedule.ScheduleOps(s, bounds)
def test_inline_mixed(): def test_inline_mixed():
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((n, ), name='A') A = tvm.placeholder((n, ), name='A')
A1 = tvm.compute(A.shape, lambda *i: A(*i) + 1, name='A1') A1 = tvm.compute(A.shape, lambda *i: A(*i) + 1, name='A1')
A2 = tvm.compute(A.shape, lambda *i: A1(*i) + 2, name='A2') A2 = tvm.compute(A.shape, lambda *i: A1(*i) + 2, name='A2')
C = tvm.compute((n,), lambda i: A2[i] + A1[i], name='C') C = tvm.compute((n,), lambda i: A2[i] + A1[i], name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
xo, xi = s[C].split(C.op.axis[0], factor=8) xo, xi = s[C].split(C.op.axis[0], factor=8)
s[A1].compute_at(s[C], xo) s[A1].compute_at(s[C], xo)
s[A2].compute_inline() s[A2].compute_inline()
...@@ -90,13 +90,13 @@ def test_inline_mixed(): ...@@ -90,13 +90,13 @@ def test_inline_mixed():
def test_schedule_cache(): def test_schedule_cache():
m = tvm.Var('m') m = tvm.var('m')
n = tvm.Var('n') n = tvm.var('n')
A = tvm.placeholder((m, n), name='A') A = tvm.placeholder((m, n), name='A')
B = tvm.placeholder((m, n), name='B') B = tvm.placeholder((m, n), name='B')
C = tvm.compute((m, n), lambda i, j: A(i, j) * B(i, j), name='C') C = tvm.compute((m, n), lambda i, j: A(i, j) * B(i, j), name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
AA = s.cache_read(A, "shared", readers=[C]) AA = s.cache_read(A, "shared", readers=[C])
CC = s.cache_write(C, "shared") CC = s.cache_write(C, "shared")
s[AA].compute_at(s[CC], CC.op.axis[0]) s[AA].compute_at(s[CC], CC.op.axis[0])
......
tests/verilog/integration/test_codegen_verilog.py
...@@ -8,7 +8,7 @@ def lower(s, args, name): ...@@ -8,7 +8,7 @@ def lower(s, args, name):
for x in args: for x in args:
assert isinstance(x, tvm.tensor.Tensor) assert isinstance(x, tvm.tensor.Tensor)
buf = tvm.Buffer(x.shape, dtype=x.dtype, name=x.op.name) buf = tvm.decl_buffer(x.shape, dtype=x.dtype, name=x.op.name)
binds[x] = buf binds[x] = buf
arg_list.append(buf) arg_list.append(buf)
s.normalize() s.normalize()
...@@ -33,7 +33,7 @@ def test_add_pipeline(): ...@@ -33,7 +33,7 @@ def test_add_pipeline():
A = tvm.placeholder((n,), name='A', dtype='int32') A = tvm.placeholder((n,), name='A', dtype='int32')
B = tvm.placeholder((n,), name='B', dtype='int32') B = tvm.placeholder((n,), name='B', dtype='int32')
C = tvm.compute(A.shape, lambda i: A[i] + B[i], name='C') C = tvm.compute(A.shape, lambda i: A[i] + B[i], name='C')
s = tvm.Schedule(C.op) s = tvm.create_schedule(C.op)
px, x = s[C].split(C.op.axis[0], nparts=1) px, x = s[C].split(C.op.axis[0], nparts=1)
s[C].bind(px, tvm.thread_axis("pipeline")) s[C].bind(px, tvm.thread_axis("pipeline"))
......
tutorials/python/README.txt 0 → 100644
.. _tutorials-index:
TVM Tutorials
=============
.. _notebook_examples:
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