gen_jit_dispatch.py

"""
To run this file by hand from the root of the PyTorch
repository, run:

python -m tools.jit.gen_jit_dispatch \
       build/aten/src/ATen/Declarations.yaml \
       $OUTPUT_DIR \
       tools/jit/templates

Where $OUTPUT_DIR is where you would like the files to be
generated.  In the full build system, OUTPUT_DIR is
torch/csrc/jit/generated/
"""

import os
import argparse
import re
from itertools import count, combinations, groupby
from ..autograd.utils import CodeTemplate, write, uninplace_api_name
from ..autograd.gen_autograd import load_aten_declarations
from collections import OrderedDict
from ..autograd.gen_autograd import RETURNS_VIEWS_OF_INPUT

# JIT has a type system of
# Scalar = int | float | bool # int is the largest int (int64_t),
# float is the largest float (double) we don't have the others because they are never held in tensors
# Type = Scalar # primitive numbers
#      | Tensor # any tensor, as defined by at::Tensor
#      | Type[] # a dynamically sized list[ of a type
#      | Scalar[N] # a homogenous fixed size scalar list, single scalars can expand to this list
#      | (Type1, Type2, ...) # a heterogenous tuple
#      | Layout | ScalarType | Device | Generator # special singleton types for built-in concepts in tensor lib

# clean up the variety of C++ types in the ATen declarations
# to be in the restricted set of types that the IR represents
# note: no default values for this map, to make it clear what types
# can be passedthrough

TYPE_MAP = {
    'std::array<bool,2>': 'bool[2]',
    'std::array<bool,3>': 'bool[3]',
    'std::array<bool,4>': 'bool[4]',
    'std::string': 'str',
    'Scalar': 'Scalar',
    'Scalar?': 'Scalar?',
    'Tensor': 'Tensor',
    'Tensor?': 'Tensor?',
    'TensorList': 'Tensor[]',
    # this appears in return values instead of TensorList
    # since TensorList is a ArrayRef in arguments but a vector
    # in returns
    'std::vector<Tensor>': 'Tensor[]',
    'IntList': 'int[]',
    'Layout': 'Layout',
    'Device': 'Device',
    'ScalarType': 'ScalarType',
    'ScalarType?': 'ScalarType?',
    'int64_t': 'int',
    'int64_t?': 'int?',
    'double': 'float',
    'bool': 'bool',
    'Generator': 'Generator?',
}


def jit_type_of(arg):
    # override for when viewing ops have already set
    # annotated jit types
    if 'jit_type' in arg:
        return arg['jit_type']
    typ = TYPE_MAP[arg['simple_type']]
    if is_sized_intlist_arg(arg):
        typ = 'int[{}]'.format(arg['size'])

    if arg.get('is_nullable') and '?' not in typ:
        typ = '{}?'.format(typ)
    return typ


# map from aten 'simple_type' to the function that will turn a tensor into
# that type
FROM_IVALUE = {
    'Device': '{}.toDevice()',
    'IntList': '{}.toIntList()->elements()',
    'Layout': '{}.toLayout()',
    'Scalar': '{}.toScalar()',
    'Scalar?': '{}.toOptional<Scalar>()',
    'ScalarType': '{}.toScalarType()',
    'ScalarType?': '{}.toOptional<ScalarType>()',
    'Tensor': '{}.toTensor()',
    'TensorList': '{}.toTensorList()->elements()',
    'bool': '{}.toBool()',
    'double': '{}.toDouble()',
    'int64_t': '{}.toInt()',
    'int64_t?': '{}.toOptional<int64_t>()',
    'std::string': '{}.toString()->string()',
    'Generator': 'nullptr',
    'std::array<bool,2>': 'as_bool_array<2>({}.toBoolListRef())',
    'std::array<bool,3>': 'as_bool_array<3>({}.toBoolListRef())',
    'std::array<bool,4>': 'as_bool_array<4>({}.toBoolListRef())',
}


def from_ivalue(arg, value):
    simple_type = arg['simple_type']
    return FROM_IVALUE[simple_type].format(value)


CALL_NAMESPACE = CodeTemplate("""\
auto result_ = at::${name}(
    ${args}
);
""")
CALL_METHOD = CodeTemplate("""\
auto result_ = (${first}).${name}(
    ${args}
);
""")
CALL_NAMESPACE_WITH_TENSOR_OPTIONS = CodeTemplate("""\
const auto options = TensorOptions()
        .dtype(${dtype})
        .layout(${layout})
        .device(${device});
auto result_ = torch::${name}(${args_with_tensor_options});
""")
CALL_METHOD_WITH_TENSOR_OPTIONS = CodeTemplate("""\
const auto options = TensorOptions()
        .dtype(${dtype})
        .layout(${layout})
        .device(${device});
auto result_ = (${first}).${name}(${args_with_tensor_options});
""")

CONSTRUCTOR = CodeTemplate("""\
[](Stack & stack) {
    autograd::profiler::RecordFunction record("${name}");
    ${lvalues}
    ${call}
    drop(stack, ${num_inputs});
    pack(stack, std::move(result_));
    return 0;
}
""")

OPERATOR = CodeTemplate("""\
Operator(
    "${signature}",
    ${op}
),
""")


blacklisted_types = {'SparseTensorRef', 'Storage', 'void*'}
default_only_types = {'Generator'}


def is_jit_arg(i, arg):
    simple_type = arg['simple_type']
    if simple_type in blacklisted_types:
        return False
    if simple_type in default_only_types and 'default' not in arg:
        return False
    if simple_type == 'Type':
        return False
    return True


def is_jit_op(decl):
    # We currently don't support functions that return nothing
    if all(r['type'] == 'void' for r in decl['returns']):
        return False

    arguments = decl['arguments']

    # there must be a single out variant
    if is_out_variant(decl) and sum([not not arg.get('output') for arg in arguments]) > 1:
        return False

    return (('namespace' in decl['method_of'] or 'Tensor' in decl['method_of']) and
            all(is_jit_arg(i, arg) for i, arg in enumerate(decl['arguments'])) and
            all(is_jit_arg(i, arg) for i, arg in enumerate(decl['returns'])))


def is_tensor_arg(arg):
    return arg['simple_type'] in {'Tensor', 'TensorList'}


def is_sized_intlist_arg(arg):
    """Returns True for arguments declared as IntList[k], but False for IntList."""
    return (arg['simple_type'] == 'IntList') and ('size' in arg)


def base_name(decl):
    name = decl['name']
    return name[:-1] if decl.get('inplace', False) else name[:-4] if name.endswith('_out') else name


def is_view(decl):
    return base_name(decl) in RETURNS_VIEWS_OF_INPUT


def is_out_variant(decl):
    return decl['name'].endswith('_out')


# for each argument in decl, the location it should appear in the
# jit schema declaration. e.g.
# arguments = [x, y, z] # the order in aten
# jit_argument_order = [2, 0, 1]
# aten::my_arg(Tensor y, Tensor z, Tensor x) # the order in schema
# used to move 'out' arguments to the end of the list
def argument_order(decl):
    return decl.get('jit_argument_order') or list(range(len(decl['arguments'])))


def gen_jit_dispatch(declarations, out, template_path):
    REGISTER_ATEN_OPS_CPP = CodeTemplate.from_file(template_path + '/register_aten_ops.cpp')

    ops = []

    def get_invocation(decl, args, num_inputs):

        # because the arg list can get lengthy we put them on a separate line
        def pack_arguments(args):
            return ',\n'.join(args)
        is_namespace_function = 'namespace' in decl['method_of']
        tensor_options_arg_index = decl.get('tensor_options_arg_index', None)
        if tensor_options_arg_index is not None:
            dtype = args[tensor_options_arg_index]
            layout = args[tensor_options_arg_index + 1]
            device = args[tensor_options_arg_index + 2]
            args_with_tensor_options = args[:tensor_options_arg_index] + \
                ['options'] + args[(tensor_options_arg_index + 3):]
            if is_namespace_function:
                return CALL_NAMESPACE_WITH_TENSOR_OPTIONS.substitute(
                    name=decl['name'], dtype=dtype, layout=layout, device=device,
                    args_with_tensor_options=pack_arguments(args_with_tensor_options))
            else:
                return CALL_METHOD_WITH_TENSOR_OPTIONS.substitute(
                    name=decl['name'], dtype=dtype, layout=layout, device=device,
                    args_with_tensor_options=pack_arguments(args_with_tensor_options[1:]),
                    first=args_with_tensor_options[0], num_inputs=num_inputs)
        else:
            if is_namespace_function:
                return CALL_NAMESPACE.substitute(name=decl['name'],
                                                 args=pack_arguments(args),
                                                 num_inputs=num_inputs)
            else:
                return CALL_METHOD.substitute(
                    name=decl['name'], first=args[0],
                    args=pack_arguments(args[1:]), num_inputs=num_inputs)

    def requires_lvalue(arg):
        return 'jit_type' in arg and arg['jit_type'] in {"Tensor!", "Tensor(a!)"}

    def emit_decl_variant(decl):
        kw_assignments = []

        # mutable arguments in aten are passed as non const references
        # these must be lvalues, so we have to put them in variables
        # before calling the function
        lvalues = []

        arguments = []
        num_inputs = len(decl['arguments'])
        op_capture = ''
        order = argument_order(decl)
        for i, arg in enumerate(decl['arguments']):
            value = from_ivalue(arg, '(std::move(peek(stack, {}, {})))'.format(order[i], num_inputs))
            if requires_lvalue(arg):
                lvalues.append('auto {} = {};\n'.format(arg['name'], value))
                value = arg['name']
            arguments.append(value)

        call = get_invocation(decl, arguments, num_inputs)

        returns = decl['returns']

        constructor = CONSTRUCTOR.substitute(name=decl['name'],
                                             call=call,
                                             kw_assignments=kw_assignments,
                                             num_inputs=num_inputs,
                                             op_capture=op_capture,
                                             lvalues=lvalues)
        return constructor

    # This function declares an order on declarations. This is necessary because
    # there is some ambiguity in the choice of overload: if an argument is overloaded
    # to accept both Scalar and Tensor, the schema with the Tensor should come first
    # TODO: this can (probably) be removed when we remove the implicit conversion
    # from Tensor -> Number.
    def sort_decls(jit_decls):
        def declkey(decl):
            # key = sum_{i < len(args)} {1 if arg is tensor else 2} * (3 ** i)
            # This is a ternary encoding where
            # 0: No argument at this position
            # 1: Tensor argument at this position
            # 2: Some other argument at this position.
            args = decl['arguments']
            result = 0
            for i in range(len(args)):
                result += (3 ** i) * (1 if args[i]['simple_type'] == 'Tensor' else 2)
            return result

        # NB: itertools.groupby requires the list be sorted.
        sorted_decls = sorted(jit_decls, key=lambda decl: decl['name'])
        grouped_decls = [list(g) for _, g in
                         groupby(sorted_decls, key=lambda decl: decl['name'])]
        return [sorted(g, key=declkey) for g in grouped_decls]

    # We need to add methods implemented manually in TensorImpl
    tensor_impl_methods = [{
        'name': name,
        'api_name': name,
        'method_of': ['Tensor'],
        'arguments': [{'name': 'self', 'simple_type': 'Tensor'}],
        'returns': [{'name': 'result', 'type': 'int64_t', 'dynamic_type': 'int64_t', 'simple_type': 'int64_t'}],
    } for name in ['sizes', 'strides', 'dim']]
    aten_decls = load_aten_declarations(declarations) + tensor_impl_methods
    jit_decls = [d for d in aten_decls if is_jit_op(d)]

    # add arguments dtype and device for functions like zeros
    def expand_options(decl, i, arg):
        if arg['simple_type'] != 'TensorOptions':
            return [arg]
        assert decl.get('tensor_options_arg_index') != i
        decl['tensor_options_arg_index'] = i
        return [
            # XXX - until we actually have first-class interpreter types for these
            # concepts, the default values to be encoded in Tensors
            # If you change this, you also need to update [TensorOptions in script]
            # in the tracer code.
            # dtype is specified as an int64_t of at::ScalarType
            {'name': 'dtype', 'simple_type': 'ScalarType', 'default': 'float', 'kwarg_only': True},
            # layout is specified as an int64_t of at::Layout
            {'name': 'layout', 'simple_type': 'Layout', 'default': 'strided', 'kwarg_only': True},
            # device is specified as an IntList of { at::Device::Type, device_id }
            {'name': 'device', 'simple_type': 'Device', 'kwarg_only': True,
                'default': '\\"cpu\\"'},
        ]

    for decl in jit_decls:
        decl['arguments'] = [a for i, arg in enumerate(decl['arguments']) for a in expand_options(decl, i, arg)]
        # add annotations about alias an mutability of arguments
        annotate_op(decl)

    # Group and sort the generated snippets to ensure that the
    # generation is deterministic
    jit_decl_groups = sort_decls(jit_decls)

    # NOTE: see Note [Sharded File] at the top of the register_aten_ops.cpp
    # template regarding sharding of the generated files.
    #
    # If you edit the number of shards here, you will also have to
    # modify generate_code.py, torch/CMakeLists.txt, and the TARGETS
    # files.
    num_shards = 3
    shards = [[] for _ in range(num_shards)]

    # ops are assigned arbitrarily but stably to a file based on hash
    for group in jit_decl_groups:
        x = sum(ord(c) for c in group[0]['name']) % num_shards
        for decl in group:
            shards[x].append(OPERATOR.substitute(signature=signature(decl),
                                                 op=emit_decl_variant(decl)))

    for i, shard in enumerate(shards):
        env = {
            'constructors': shard,
        }
        write(out, 'register_aten_ops_%d.cpp' % i, REGISTER_ATEN_OPS_CPP, env)


default_map = {'{}': 'None', 'nullptr': 'None', 'c10::nullopt': 'None'}


def annotate_op(decl):
    # insert alias annotations into viewing operators
    if decl.get('inplace') or is_out_variant(decl):
        first_arg = decl['arguments'][0]
        assert(jit_type_of(first_arg) == 'Tensor')
        first_arg['jit_type'] = 'Tensor(a!)'
        first_ret = decl['returns'][0]
        assert(jit_type_of(first_ret) == 'Tensor')
        first_ret['jit_type'] = 'Tensor(a!)'
        if is_out_variant(decl):
            assert(first_arg['output'])
            # the output variant must go at the end
            # note: this is an annoying side effect of using a single '*'
            # to denote kwarg_only
            nargs = len(decl['arguments'])
            decl['jit_argument_order'] = [nargs - 1] + list(range(nargs - 1))
    elif is_view(decl):
        first_arg = decl['arguments'][0]
        assert jit_type_of(first_arg) == 'Tensor'
        first_arg['jit_type'] = 'Tensor(a)'
        first_ret = decl['returns'][0]
        ret_type = jit_type_of(first_ret)
        if ret_type == 'Tensor[]':
            first_ret['jit_type'] = 'Tensor(a)[]'
        elif ret_type == 'Tensor':
            first_ret['jit_type'] = 'Tensor(a)'


def is_kwarg_only(a):
    return a.get('kwarg_only') or a.get('output')


def signature(decl):
    def format_arg(arg):
        name = arg['name'] if not arg.get('output') else 'out'
        typ = jit_type_of(arg)
        decl = '{} {}'.format(typ, name)
        if 'default' in arg:
            # clean up initializer lists {{true, true}} -> [true, true]
            default = str(arg['default']) \
                .replace('{{', '[') \
                .replace('}}', ']') \
                .replace('true', 'True') \
                .replace('false', 'False') \
                .replace('Reduction::Mean', 'Mean') \
                .replace('{}', 'None' if is_tensor_arg(arg) else '[]') \
                .replace('{', '[') \
                .replace('}', ']')

            default = default_map.get(default, default)
            decl = '{}={}'.format(decl, default)
        return decl

    args = []
    kwarg_only = False

    ordered_arguments = sorted(zip(argument_order(decl), decl['arguments']))
    for _, a in ordered_arguments:
        if not kwarg_only and is_kwarg_only(a):
            args.append('*')
            kwarg_only = True
        args.append(format_arg(a))

    arg_list = ', '.join(args)
    if len(decl['returns']) == 1:
        ret_list = jit_type_of(decl['returns'][0])
    else:
        ret_list = '({})'.format(', '.join(jit_type_of(r) for r in decl['returns']))
    name = decl['name'] if not is_out_variant(decl) else decl['name'][:-4]
    constructed_string = 'aten::{}({}) -> {}'.format(name, arg_list, ret_list)
    # If matches_jit_signature has been specified the signature constructed from the
    # declared attributes should match the raw string passed through. In the
    # case of native_functions.yaml, func should match the generated signature,
    # if matches_jit_signature is true. This is used to track and verify the alignment
    # of native_function.yaml's function schema with that used in this parse.
    if decl.get('matches_jit_signature'):
        assert(constructed_string == decl['schema_string']), \
            decl['schema_string'] + ' is flagged as JIT signature compliant' + \
            ', but does not match the signature ' + constructed_string
        return decl['schema_string']
    return constructed_string


def main():
    parser = argparse.ArgumentParser(
        description='Generate JIT op dispatch')
    parser.add_argument('declarations', metavar='DECL',
                        help='path to Declarations.yaml')
    parser.add_argument('out', metavar='OUT',
                        help='path to output directory')
    parser.add_argument('template_path', metavar='TEMPLATE_PATH',
                        help='path to templates directory')
    args = parser.parse_args()
    gen_jit_dispatch(args.declarations, args.out, args.template_path)


if __name__ == '__main__':
    main()