#!/bin/env python
# Copyright (c) 2016-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
import sys
import yaml
import argparse
import os
from copy import deepcopy
from typing import Dict, List, Set
parser = argparse.ArgumentParser()
parser.add_argument("--template_dir", default=".", help="where template.h is")
parser.add_argument("--yaml_dir", default="aten/src/ATen/ATen",
help="where ATen yaml files are")
parser.add_argument("--output_prefix", default="", help="")
parser.add_argument(
"--install_dir", default=".", help="where to put generated file")
parser.add_argument("--aten_root", default="", help="root directory of aten")
args, _ = parser.parse_known_args()
if args.aten_root:
if not os.path.exists(args.aten_root):
raise ValueError('aten_root ({}) does not exist'.format(
args.aten_root))
sys.path.insert(0, os.path.join(args.aten_root, '..'))
from tools.codegen.code_template import CodeTemplate as CT
else:
from tools.codegen.code_template import CodeTemplate as CT # type: ignore[import,no-redef]
OP_TEMPLATE = CT.from_file(
os.path.join(args.template_dir, 'aten_op_template.h'))
try:
# use faster C loader if available
from yaml import CLoader as Loader
except ImportError:
from yaml import Loader # type: ignore[misc]
def write(filename, s):
with open(filename, "w") as f:
f.write(s)
def read(filename):
with open(filename, "r") as f:
return f.read()
def value_has_tensors(v):
# Sparse shouldn't appear in public API, seems to be temporary bug
return "Tensor" in v['dynamic_type'] and "Sparse" not in v['dynamic_type']
def value_is_tensor_type(v):
return value_has_tensors(v) and v['dynamic_type'] not in ['TensorList', 'const c10::List<c10::optional<Tensor>> &']
# for each aten type, how do we handle a return value of that type?
RETURN_MAP = {
'Tensor': 'assignTo(Output(${offset}),${output});',
'Scalar': 'assignTo(Output(${offset}),${output}.type(), ${output});',
'bool': 'assignToValue<int64_t>(Output(${offset}),${output});',
'int64_t': 'assignToValue<int64_t>(Output(${offset}),${output});',
'std::vector<Tensor>': 'assignListStartingAt(${offset}, ${output});',
}
# for each non-Tensor aten argument, how to we read it from caffe2's
# attribute list. Most of these call runtime functions defined in the
# template class.
ARGUMENT_MAP = {
'Scalar': 'at::Scalar ${arg} = readScalarAttribute("${arg}");',
'bool': 'bool ${arg} = readAttribute<int64_t>("${arg}");',
'int': 'int ${arg} = readAttribute<int64_t>("${arg}");',
'double': 'double ${arg} = readAttribute<float>("${arg}");',
'int64_t': 'int64_t ${arg} = readAttribute<int64_t>("${arg}");',
'IntArrayRef': 'auto ${arg} = readIntArrayRef("${arg}");',
'std::array<bool,2>': 'auto ${arg} = readBoolMask<2>("${arg}");',
'std::array<bool,3>': 'auto ${arg} = readBoolMask<3>("${arg}");',
}
# for BC reasons we want to route some of the functions to different
# implementations
SPECIAL_IMPLEMENTATIONS = {
'index': 'internal::index_with_uint8_handling',
}
def expand(o):
num_defaults = sum(1 if 'default' in arg else 0 for arg in o['arguments'])
results = [o]
for i in range(0, num_defaults):
# last num_default values should be default
assert('default' in o['arguments'][-(i + 1)])
v = deepcopy(o)
v['arguments'] = v['arguments'][:-(i + 1)]
results.append(v)
return results
# filter the list of declarations removing things we cannot support
def supports(o, factory_methods):
# Ignore all families (!) of functions that have TensorOptions (i.e. tensor factory methods).
if o['name'] in factory_methods:
if factory_methods[o['name']] == 0:
print("Skipping {} because it is a factory method".format(o['name']))
factory_methods[o['name']] += 1
return False
# skip all in-place operators for now since aten cannot Resize
# caffe2 memory inside an operator
if o['inplace']:
return False
# _out variants also work in-place on arguments taken as destinations
# we also cannot handle these because aten cannot resize caffe2 Tensors
if "_out" in o['name']:
return False
# skip if no return, previously it is 'void'
if len(o['returns']) == 0:
return False
# skip return types we cannot handle
for ret in o['returns']:
if not value_has_tensors(ret) and ret['type'] not in RETURN_MAP:
print("Skipping {} Because of Ret: {} ({})".format(
o['name'], ret['type'], ret['dynamic_type']))
return False
# skip arguments we cannot handle
for arg in o['arguments']:
if not value_has_tensors(arg) and arg['type'] not in ARGUMENT_MAP:
print("Skipping {} Because of Arg: {} ({}) ".format(
o['name'], arg['type'], arg['dynamic_type']))
return False
return True
# template for each potential operator.
# each operator has an integer 'key' associated with it, and
# a lambda that defines the operator
# non-tensor attributes are created in ${initialization}
# and then saved as arguments to the lambda
# Inputs/Outputs are read inside the lambda
#
# each implementation is defined in a separate method annotated with
# C10_NOINLINE to avoid inlining into the ATenOp constructor, which would
# trigger pathological compile times.
IMPLEMENTATION_TEMPLATE = CT("""\
C10_NOINLINE void implementation_${key}() { // ${name}
${initialization}
run_op = [=] {
at::AutoNonVariableTypeMode guard;
${statements}
auto the_result = ${invocation};
${assignments}
return true;
};
}
""")
CASE_TEMPLATE = CT("""\
case ${key}: // ${name}
implementation_${key}();
break;
""")
ASSIGN_CHECK_SIZE_TEMPLATE = CT("""\
if(OutputSize() > ${offset}) {${assignment}}
""")
def get_output(o, i):
if len(o['returns']) == 1:
return 'the_result'
else:
return 'std::get<{}>(the_result)'.format(i)
def attribute_names(o):
return sorted([a['name'] for a in o['arguments'] if not value_has_tensors(a)])
def required_attribute_names(o):
return sorted([a['name'] for a in o['arguments'] if not value_has_tensors(a) and 'default' not in a])
def self_as_first_argument(arguments):
return ([a for a in arguments if a['name'] == 'self'] +
[a for a in arguments if a['name'] != 'self'])
def get_num_inputs(o):
args = 0
for a in o['arguments']:
if a['type'] in ['TensorList', 'const c10::List<c10::optional<Tensor>> &']:
return '*'
elif value_has_tensors(a):
args += 1
return str(args)
def find_factory_methods(decls):
factory_methods = {}
for o in decls:
if any(arg['dynamic_type'] == 'TensorOptions' for arg in o['arguments']):
factory_methods[o['name']] = 0
return factory_methods
def emit_assignments(o, env):
for i, r in enumerate(o['returns']):
t = RETURN_MAP[r['type'] if not value_is_tensor_type(r) else 'Tensor']
assignment = CT(t).substitute(env, offset=i, output=get_output(o, i))
check_size_assignment = ASSIGN_CHECK_SIZE_TEMPLATE.substitute(env, offset=i, assignment=assignment)
env['assignments'].append(check_size_assignment)
if __name__ == '__main__':
decls = yaml.load(read(os.path.join(args.yaml_dir, 'Declarations.yaml')), Loader=Loader)
factory_methods = find_factory_methods(decls)
filtered = [expanded for o in decls for expanded in expand(o) if supports(expanded, factory_methods)]
top_env: Dict[str, List] = {
'mappings': [],
'implementations': [],
'cases': [],
}
seen: Set[str] = set()
key = 0
for o in filtered:
# [DESCRIPTORS]
# each option is associated with a descriptor string that is used
# to figure out which version of an op is being used:
# The format is:
# opname-num_inputs-attribute_1-attribute2
# Example:
# lerp-2-weight
# the operator lerp takes 2 arguments and has the attribute weight
attr_names = attribute_names(o)
num_inputs = get_num_inputs(o)
descriptor = '-'.join([o['name']] + attr_names + [num_inputs])
if descriptor in seen:
continue
seen.add(descriptor)
# map from descriptor string to the integer key in the switch statements
# that initializes the operators
top_env['mappings'].append('{{ "{}", {} }},'.format(descriptor, key))
env = {
'name': o['name'],
'statements': [],
'arguments': [],
'assignments': [],
'initialization': [],
'key': str(key),
}
if 'namespace' not in o['method_of'] and 'Tensor' not in o['method_of']:
# methods on type like 'ones' or 'zeros' always take a
# string attribute that is translated into the at::Type object
# e.g. "Float" is at::kFloat
assert('Type' in o['method_of'])
static_tensor_inputs = sum(arg['type'] not in ['TensorList', 'const c10::List<c10::optional<Tensor>> &'] and value_is_tensor_type(arg) for arg in o['arguments'])
has_tensorlist = any(arg['type'] in ['TensorList', 'const c10::List<c10::optional<Tensor>> &'] for arg in o['arguments'])
if has_tensorlist:
tensorlist_idx = [i for i, arg in enumerate(o['arguments']) if arg['type'] in ['TensorList', 'const c10::List<c10::optional<Tensor>> &']][0]
real_inputs = 0
for i, arg in enumerate(o['arguments']):
env['arguments'].append(arg['name'])
# Pretend the flat argument list is a stack where the end is the top.
view_length = 'InputSize()' if has_tensorlist and i < tensorlist_idx else static_tensor_inputs
if arg['type'] == 'TensorList':
# NOTE: do not advance real_inputs here. After this we will
# switch to indexing the "stack" from the end
env['statements'].append(
'auto {} = peekSlice({}, InputSize() - {}, InputSize());'
.format(arg['name'], real_inputs, static_tensor_inputs))
elif arg['type'] == 'const c10::List<c10::optional<Tensor>> &':
# NOTE: do not advance real_inputs here. After this we will
# switch to indexing the "stack" from the end
env['statements'].append(
'auto {} = peekSliceOptionals({}, InputSize() - {}, InputSize());'
.format(arg['name'], real_inputs, static_tensor_inputs))
elif value_is_tensor_type(arg):
# load tensor inputs from Caffe2
env['statements'].append(
'auto {} = peek({}, {});'.format(arg['name'], real_inputs, view_length))
real_inputs += 1
else:
init = CT(ARGUMENT_MAP[arg['type']]).substitute(env, arg=arg['name'])
env['initialization'].append(init)
emit_assignments(o, env)
if o['name'] in SPECIAL_IMPLEMENTATIONS:
env['invocation'] = "{}({})".format(SPECIAL_IMPLEMENTATIONS[o['name']], ','.join(env['arguments']))
elif 'namespace' in o['method_of']:
env['invocation'] = CT("at::${name}(${arguments})").substitute(env)
else:
assert('Tensor' in o['method_of'])
env['invocation'] = "self.{}({})".format(
o['name'], ', '.join(env['arguments'][1:]))
top_env['implementations'].append(IMPLEMENTATION_TEMPLATE.substitute(env))
top_env['cases'].append(CASE_TEMPLATE.substitute(env))
key += 1
write(os.path.join(args.install_dir, args.output_prefix + "aten_op.h"), OP_TEMPLATE.substitute(top_env))