610 lines
22 KiB
Python
610 lines
22 KiB
Python
|
import argparse
|
||
|
|
import os
|
||
|
|
import pathlib
|
||
|
|
import re
|
||
|
|
from collections import Counter, defaultdict, namedtuple
|
||
|
|
from typing import Dict, List, Optional, Sequence, Set, Union
|
||
|
|
|
||
|
|
import yaml
|
||
|
|
|
||
|
|
import torchgen.api.dispatcher as dispatcher
|
||
|
|
import torchgen.dest as dest
|
||
|
|
from torchgen.api.types import DispatcherSignature
|
||
|
|
from torchgen.code_template import CodeTemplate
|
||
|
|
from torchgen.context import native_function_manager
|
||
|
|
from torchgen.gen import get_grouped_native_functions, parse_native_yaml
|
||
|
|
from torchgen.model import (
|
||
|
|
BackendIndex,
|
||
|
|
BackendMetadata,
|
||
|
|
DispatchKey,
|
||
|
|
NativeFunction,
|
||
|
|
NativeFunctionsGroup,
|
||
|
|
OperatorName,
|
||
|
|
)
|
||
|
|
from torchgen.selective_build.selector import SelectiveBuilder
|
||
|
|
from torchgen.utils import concatMap, context, FileManager, NamespaceHelper, Target
|
||
|
|
from torchgen.yaml_utils import YamlLoader
|
||
|
|
|
||
|
|
|
||
|
|
# Parses the external backend's yaml, and adds a new BackendIndex for the backend's dispatch key.
|
||
|
|
# Returns a Tuple of (backend_key, autograd_key, cpp_namespace, updated BackendIndex mapping)
|
||
|
|
ParsedExternalYaml = namedtuple(
|
||
|
|
"ParsedExternalYaml",
|
||
|
|
["backend_key", "autograd_key", "class_name", "cpp_namespace", "backend_indices"],
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def parse_backend_yaml(
|
||
|
|
backend_yaml_path: str,
|
||
|
|
grouped_native_functions: Sequence[Union[NativeFunction, NativeFunctionsGroup]],
|
||
|
|
backend_indices: Dict[DispatchKey, BackendIndex],
|
||
|
|
) -> ParsedExternalYaml:
|
||
|
|
native_functions_map: Dict[OperatorName, NativeFunction] = {
|
||
|
|
f.func.name: f
|
||
|
|
for f in concatMap(
|
||
|
|
lambda f: [f] if isinstance(f, NativeFunction) else list(f.functions()),
|
||
|
|
grouped_native_functions,
|
||
|
|
)
|
||
|
|
}
|
||
|
|
|
||
|
|
with open(backend_yaml_path) as f:
|
||
|
|
yaml_values = yaml.load(f, Loader=YamlLoader)
|
||
|
|
assert isinstance(yaml_values, dict)
|
||
|
|
|
||
|
|
valid_keys = [
|
||
|
|
"backend",
|
||
|
|
"class_name",
|
||
|
|
"cpp_namespace",
|
||
|
|
"extra_headers",
|
||
|
|
"supported",
|
||
|
|
"autograd",
|
||
|
|
"full_codegen",
|
||
|
|
"non_native",
|
||
|
|
"ir_gen",
|
||
|
|
"symint",
|
||
|
|
]
|
||
|
|
|
||
|
|
backend = yaml_values.pop("backend", None)
|
||
|
|
assert backend is not None, 'You must provide a value for "backend"'
|
||
|
|
|
||
|
|
class_name = yaml_values.pop("class_name", None)
|
||
|
|
|
||
|
|
cpp_namespace = yaml_values.pop("cpp_namespace", None)
|
||
|
|
assert cpp_namespace is not None, 'You must provide a value for "cpp_namespace"'
|
||
|
|
|
||
|
|
# Mostly just defaulting to false to stick with LazyTensor convention.
|
||
|
|
use_out_as_primary = yaml_values.pop("use_out_as_primary", False)
|
||
|
|
assert isinstance(
|
||
|
|
use_out_as_primary, bool
|
||
|
|
), f"You must provide either True or False for use_out_as_primary. Provided: {use_out_as_primary}"
|
||
|
|
|
||
|
|
use_device_guard = yaml_values.pop("device_guard", False)
|
||
|
|
assert isinstance(
|
||
|
|
use_device_guard, bool
|
||
|
|
), f"You must provide either True or False for device_guard. Provided: {use_device_guard}"
|
||
|
|
|
||
|
|
supported = yaml_values.pop("supported", [])
|
||
|
|
if supported is None:
|
||
|
|
supported = [] # Allow an empty list of supported ops
|
||
|
|
assert isinstance(
|
||
|
|
supported, list
|
||
|
|
), f'expected "supported" to be a list, but got: {supported} (of type {type(supported)})'
|
||
|
|
|
||
|
|
symint = yaml_values.pop("symint", [])
|
||
|
|
if symint is None:
|
||
|
|
symint = [] # Allow an empty list of symint ops
|
||
|
|
assert isinstance(
|
||
|
|
symint, list
|
||
|
|
), f'expected "symint" to be a list, but got: {supported} (of type {type(supported)})'
|
||
|
|
symint_set = set(symint)
|
||
|
|
|
||
|
|
supported_autograd = yaml_values.pop("autograd", [])
|
||
|
|
assert isinstance(
|
||
|
|
supported_autograd, list
|
||
|
|
), f'expected "autograd" to be a list, but got: {supported_autograd}'
|
||
|
|
|
||
|
|
# full_codegen is ignored by parse_backend_yaml, and re-parsed in gen_lazy_tensor.py
|
||
|
|
full_codegen = yaml_values.pop("full_codegen", [])
|
||
|
|
supported.extend(full_codegen)
|
||
|
|
|
||
|
|
# non_native is ignored by parse_backend_yaml, and re-parsed in gen_lazy_tensor.py
|
||
|
|
non_native = yaml_values.pop("non_native", {})
|
||
|
|
|
||
|
|
# ir_gen is ignored by parse_backend_yaml, and re-parsed in gen_lazy_tensor.py
|
||
|
|
_ = yaml_values.pop("ir_gen", {})
|
||
|
|
|
||
|
|
assert (
|
||
|
|
len(yaml_values.keys()) == 0
|
||
|
|
), f'{backend_yaml_path} contains unexpected keys: {", ".join(yaml_values.keys())}. \
|
||
|
|
Only the following keys are supported: {", ".join(valid_keys)}'
|
||
|
|
|
||
|
|
def create_backend_index(
|
||
|
|
backend_ops: List[str],
|
||
|
|
symint_ops: Set[str],
|
||
|
|
dispatch_key: DispatchKey,
|
||
|
|
*,
|
||
|
|
use_out_as_primary: bool,
|
||
|
|
use_device_guard: bool,
|
||
|
|
) -> BackendIndex:
|
||
|
|
metadata: Dict[OperatorName, BackendMetadata] = {}
|
||
|
|
for op in backend_ops:
|
||
|
|
op_name = OperatorName.parse(op)
|
||
|
|
assert (
|
||
|
|
op_name in native_functions_map
|
||
|
|
), f"Found an invalid operator name: {op_name}"
|
||
|
|
# See Note [External Backends Follow Dispatcher API]
|
||
|
|
kernel_name = dispatcher.name(native_functions_map[op_name].func)
|
||
|
|
if op in symint_ops:
|
||
|
|
kernel_name += "_symint"
|
||
|
|
# TODO: allow structured external backends later.
|
||
|
|
m = BackendMetadata(
|
||
|
|
kernel=kernel_name, structured=False, cpp_namespace=cpp_namespace
|
||
|
|
)
|
||
|
|
metadata[op_name] = m
|
||
|
|
return BackendIndex(
|
||
|
|
dispatch_key=dispatch_key,
|
||
|
|
use_out_as_primary=use_out_as_primary,
|
||
|
|
external=True,
|
||
|
|
device_guard=use_device_guard,
|
||
|
|
index=metadata,
|
||
|
|
)
|
||
|
|
|
||
|
|
backend_key: Optional[DispatchKey] = None
|
||
|
|
if len(supported) > 0:
|
||
|
|
with context(
|
||
|
|
lambda: f'The provided value for "backend" must be a valid DispatchKey, but got {backend}.'
|
||
|
|
):
|
||
|
|
backend_key = DispatchKey.parse(backend)
|
||
|
|
|
||
|
|
backend_idx = create_backend_index(
|
||
|
|
supported,
|
||
|
|
symint_set,
|
||
|
|
backend_key,
|
||
|
|
use_out_as_primary=use_out_as_primary,
|
||
|
|
use_device_guard=use_device_guard,
|
||
|
|
)
|
||
|
|
assert backend_key not in backend_indices
|
||
|
|
backend_indices[backend_key] = backend_idx
|
||
|
|
|
||
|
|
autograd_key: Optional[DispatchKey] = None
|
||
|
|
if len(supported_autograd) > 0:
|
||
|
|
with context(
|
||
|
|
lambda: f'The "autograd" key was specified, which indicates that you would like to override \
|
||
|
|
the behavior of autograd for some operators on your backend. However "Autograd{backend}" is not a valid DispatchKey.'
|
||
|
|
):
|
||
|
|
autograd_key = DispatchKey.parse(f"Autograd{backend}")
|
||
|
|
|
||
|
|
autograd_idx = create_backend_index(
|
||
|
|
supported_autograd,
|
||
|
|
symint_set,
|
||
|
|
autograd_key,
|
||
|
|
use_out_as_primary=use_out_as_primary,
|
||
|
|
use_device_guard=use_device_guard,
|
||
|
|
)
|
||
|
|
assert autograd_key not in backend_indices
|
||
|
|
backend_indices[autograd_key] = autograd_idx
|
||
|
|
|
||
|
|
for g in grouped_native_functions:
|
||
|
|
if isinstance(g, NativeFunction):
|
||
|
|
forward_kernels = (
|
||
|
|
[]
|
||
|
|
if backend_key is None
|
||
|
|
else [
|
||
|
|
m
|
||
|
|
for m in [backend_indices[backend_key].get_kernel(g)]
|
||
|
|
if m is not None
|
||
|
|
]
|
||
|
|
)
|
||
|
|
backward_kernels = (
|
||
|
|
[]
|
||
|
|
if autograd_key is None
|
||
|
|
else [
|
||
|
|
m
|
||
|
|
for m in [backend_indices[autograd_key].get_kernel(g)]
|
||
|
|
if m is not None
|
||
|
|
]
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
forward_kernels = (
|
||
|
|
[]
|
||
|
|
if backend_key is None
|
||
|
|
else [
|
||
|
|
m
|
||
|
|
for m in [
|
||
|
|
backend_indices[backend_key].get_kernel(f)
|
||
|
|
for f in g.functions()
|
||
|
|
]
|
||
|
|
if m is not None
|
||
|
|
]
|
||
|
|
)
|
||
|
|
backward_kernels = (
|
||
|
|
[]
|
||
|
|
if autograd_key is None
|
||
|
|
else [
|
||
|
|
m
|
||
|
|
for m in [
|
||
|
|
backend_indices[autograd_key].get_kernel(f)
|
||
|
|
for f in g.functions()
|
||
|
|
]
|
||
|
|
if m is not None
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
forward_kernels = [f for f in forward_kernels if f is not None]
|
||
|
|
backward_kernels = [f for f in backward_kernels if f is not None]
|
||
|
|
assert (
|
||
|
|
len(forward_kernels) == 0 or len(backward_kernels) == 0
|
||
|
|
), f'Currently, all variants of an op must either be registered to a backend key, or to a backend\'s \
|
||
|
|
autograd key. They cannot be mix and matched. If this is something you need, feel free to create an issue! \
|
||
|
|
{forward_kernels[0].kernel} is listed under "supported", but {backward_kernels[0].kernel} is listed under "autograd".'
|
||
|
|
|
||
|
|
return ParsedExternalYaml(
|
||
|
|
backend_key, autograd_key, class_name, cpp_namespace, backend_indices
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def error_on_missing_kernels(
|
||
|
|
native_functions: Sequence[NativeFunction],
|
||
|
|
backend_indices: Dict[DispatchKey, BackendIndex],
|
||
|
|
backend_key: DispatchKey,
|
||
|
|
autograd_key: Optional[DispatchKey],
|
||
|
|
class_name: str,
|
||
|
|
kernel_defn_file_path: str,
|
||
|
|
full_codegen: Optional[List[OperatorName]] = None,
|
||
|
|
) -> None:
|
||
|
|
try:
|
||
|
|
with open(kernel_defn_file_path) as f:
|
||
|
|
backend_defns = f.read()
|
||
|
|
except OSError as e:
|
||
|
|
raise AssertionError(
|
||
|
|
f"Unable to read from the specified impl_path file: {kernel_defn_file_path}"
|
||
|
|
) from e
|
||
|
|
|
||
|
|
if full_codegen is None:
|
||
|
|
full_codegen = []
|
||
|
|
|
||
|
|
indices = [backend_indices[backend_key].index] + (
|
||
|
|
[] if autograd_key is None else [backend_indices[autograd_key].index]
|
||
|
|
)
|
||
|
|
# Quick mapping from each OperatorName used by the external backend
|
||
|
|
# to its backend kernel name
|
||
|
|
expected_backend_op_names: Dict[OperatorName, str] = dict(
|
||
|
|
list(
|
||
|
|
concatMap(
|
||
|
|
lambda index: [
|
||
|
|
(op_name, metadata.kernel) for op_name, metadata in index.items()
|
||
|
|
],
|
||
|
|
indices,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
expected_backend_native_funcs: List[NativeFunction] = [
|
||
|
|
f
|
||
|
|
for f in native_functions
|
||
|
|
if f.func.name in expected_backend_op_names.keys()
|
||
|
|
and f.func.name not in full_codegen
|
||
|
|
]
|
||
|
|
expected_backend_kernel_name_counts: Dict[str, List[NativeFunction]] = defaultdict(
|
||
|
|
list
|
||
|
|
)
|
||
|
|
for native_f in expected_backend_native_funcs:
|
||
|
|
expected_backend_kernel_name_counts[
|
||
|
|
expected_backend_op_names[native_f.func.name]
|
||
|
|
].append(native_f)
|
||
|
|
|
||
|
|
# This just looks for lines containing "foo(", and assumes that the kernel foo has been implemented.
|
||
|
|
# It might cause false negatives (we won't catch all cases), but that's ok - if we catch a missing kernel
|
||
|
|
# here, then we get a nicer error message. If we miss it, you get a linker error.
|
||
|
|
kernel_defn_regex = rf"(.*){class_name}::\s*([\w\d]*)\("
|
||
|
|
actual_backend_kernel_name_counts = Counter(
|
||
|
|
# A bit unwieldy (this could probably be moved into regex),
|
||
|
|
# but we don't want to include kernel names that come from function calls,
|
||
|
|
# like "return torch_xla::XLANativeFunctions::empty_strided_symint(...)".
|
||
|
|
# Easy check is to ignore any lines with colons before the class name.
|
||
|
|
[
|
||
|
|
y
|
||
|
|
for (x, y) in re.findall(kernel_defn_regex, backend_defns)
|
||
|
|
if not x.endswith(":")
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
missing_kernels_err_msg = ""
|
||
|
|
for expected_name, funcs in expected_backend_kernel_name_counts.items():
|
||
|
|
expected_overload_count = len(funcs)
|
||
|
|
actual_overload_count = actual_backend_kernel_name_counts[expected_name]
|
||
|
|
if expected_overload_count != actual_overload_count:
|
||
|
|
|
||
|
|
def create_decl(f: NativeFunction) -> str:
|
||
|
|
with native_function_manager(f):
|
||
|
|
return DispatcherSignature.from_schema(f.func).decl()
|
||
|
|
|
||
|
|
expected_schemas_str = "\n".join([create_decl(f) for f in funcs])
|
||
|
|
missing_kernels_err_msg += f"""
|
||
|
|
{class_name} is missing a kernel definition for {expected_name}. We found {actual_overload_count} kernel(s) with that name,
|
||
|
|
but expected {expected_overload_count} kernel(s). The expected function schemas for the missing operator are:
|
||
|
|
{expected_schemas_str}
|
||
|
|
|
||
|
|
"""
|
||
|
|
assert missing_kernels_err_msg == "", missing_kernels_err_msg
|
||
|
|
|
||
|
|
|
||
|
|
def main() -> None:
|
||
|
|
parser = argparse.ArgumentParser(description="Generate backend stub files")
|
||
|
|
parser.add_argument(
|
||
|
|
"-s",
|
||
|
|
"--source-yaml",
|
||
|
|
"--source_yaml",
|
||
|
|
help="path to source yaml file containing operator external definitions",
|
||
|
|
)
|
||
|
|
parser.add_argument("-o", "--output-dir", "--output_dir", help="output directory")
|
||
|
|
parser.add_argument(
|
||
|
|
"--dry-run", "--dry_run", type=bool, default=False, help="output directory"
|
||
|
|
)
|
||
|
|
parser.add_argument(
|
||
|
|
"--impl-path",
|
||
|
|
"--impl_path",
|
||
|
|
type=str,
|
||
|
|
default=None,
|
||
|
|
help="path to the source C++ file containing kernel definitions",
|
||
|
|
)
|
||
|
|
options = parser.parse_args()
|
||
|
|
|
||
|
|
run(options.source_yaml, options.output_dir, options.dry_run, options.impl_path)
|
||
|
|
|
||
|
|
|
||
|
|
def gen_dispatchkey_nativefunc_headers(
|
||
|
|
fm: FileManager,
|
||
|
|
class_name: str,
|
||
|
|
cpp_namespace: str,
|
||
|
|
backend_indices: Dict[DispatchKey, BackendIndex],
|
||
|
|
grouped_native_functions: Sequence[Union[NativeFunction, NativeFunctionsGroup]],
|
||
|
|
backend_dispatch_key: DispatchKey,
|
||
|
|
autograd_dispatch_key: Optional[DispatchKey],
|
||
|
|
backend_name: str = "",
|
||
|
|
) -> None:
|
||
|
|
assert class_name is not None
|
||
|
|
generated_comment = (
|
||
|
|
"Autogenerated file by gen_backend_stubs.py. Do not edit directly!"
|
||
|
|
)
|
||
|
|
|
||
|
|
# Convert to a set first to remove duplicate kernel names.
|
||
|
|
# Backends are allowed to repeat kernel names; only generate the declaration once!
|
||
|
|
# Sort for deterministic output.
|
||
|
|
backend_declarations = sorted(
|
||
|
|
set(
|
||
|
|
concatMap(
|
||
|
|
lambda f: dest.compute_native_function_declaration(
|
||
|
|
f, backend_indices[backend_dispatch_key]
|
||
|
|
),
|
||
|
|
grouped_native_functions,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
autograd_declarations = sorted(
|
||
|
|
set(
|
||
|
|
concatMap(
|
||
|
|
lambda f: []
|
||
|
|
if autograd_dispatch_key is None
|
||
|
|
else dest.compute_native_function_declaration(
|
||
|
|
f, backend_indices[autograd_dispatch_key]
|
||
|
|
),
|
||
|
|
grouped_native_functions,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
ns_helper = NamespaceHelper(cpp_namespace)
|
||
|
|
fm.write_with_template(
|
||
|
|
f"{backend_dispatch_key}NativeFunctions.h",
|
||
|
|
"DispatchKeyNativeFunctions.h",
|
||
|
|
lambda: {
|
||
|
|
"generated_comment": generated_comment,
|
||
|
|
"namespace_prologue": ns_helper.prologue,
|
||
|
|
"class_name": class_name,
|
||
|
|
"namespace_epilogue": ns_helper.epilogue,
|
||
|
|
"dispatch_declarations": backend_declarations + autograd_declarations,
|
||
|
|
"BackendName": backend_name,
|
||
|
|
"DispatchKey": backend_dispatch_key,
|
||
|
|
},
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def gen_dispatcher_registrations(
|
||
|
|
fm: FileManager,
|
||
|
|
output_dir: str,
|
||
|
|
class_name: str,
|
||
|
|
backend_indices: Dict[DispatchKey, BackendIndex],
|
||
|
|
grouped_native_functions: Sequence[Union[NativeFunction, NativeFunctionsGroup]],
|
||
|
|
backend_dispatch_key: DispatchKey,
|
||
|
|
dispatch_key: DispatchKey,
|
||
|
|
selector: "SelectiveBuilder",
|
||
|
|
# build_in_tree is true for lazy TS backend and affects include paths, not used for external backends
|
||
|
|
build_in_tree: bool = False,
|
||
|
|
per_operator_headers: bool = False,
|
||
|
|
backend_name: str = "",
|
||
|
|
eager_registration: bool = True,
|
||
|
|
) -> None:
|
||
|
|
headers = [
|
||
|
|
f"{output_dir}/{backend_dispatch_key}NativeFunctions.h",
|
||
|
|
]
|
||
|
|
if build_in_tree:
|
||
|
|
external_backend_headers_str = "\n".join(f"#include <{h}>" for h in headers)
|
||
|
|
else:
|
||
|
|
external_backend_headers_str = "\n".join(f'#include "{h}"' for h in headers)
|
||
|
|
|
||
|
|
assert class_name is not None
|
||
|
|
backend_index = backend_indices[dispatch_key]
|
||
|
|
|
||
|
|
dispatch_registrations_body = list(
|
||
|
|
concatMap(
|
||
|
|
dest.RegisterDispatchKey(
|
||
|
|
backend_index,
|
||
|
|
Target.REGISTRATION,
|
||
|
|
selector,
|
||
|
|
rocm=False,
|
||
|
|
symint=True,
|
||
|
|
class_method_name=f"{class_name}",
|
||
|
|
skip_dispatcher_op_registration=False,
|
||
|
|
),
|
||
|
|
grouped_native_functions,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
newline = "\n"
|
||
|
|
ns_helper = NamespaceHelper(namespace_str="at")
|
||
|
|
deferred_dispatch_registrations = ""
|
||
|
|
static_init_dispatch_registrations = ""
|
||
|
|
if eager_registration:
|
||
|
|
static_template = CodeTemplate(
|
||
|
|
"""\
|
||
|
|
TORCH_LIBRARY_IMPL(aten, $dispatch_key, m) {
|
||
|
|
$dispatch_registrations_body
|
||
|
|
};"""
|
||
|
|
)
|
||
|
|
static_init_dispatch_registrations = static_template.substitute(
|
||
|
|
dispatch_key=dispatch_key,
|
||
|
|
dispatch_registrations_body=dispatch_registrations_body,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
deferred_template = CodeTemplate(
|
||
|
|
"""\
|
||
|
|
TORCH_API void Register${backend_name}${dispatch_key}NativeFunctions();
|
||
|
|
TORCH_API void Register${backend_name}${dispatch_key}NativeFunctions() {
|
||
|
|
static auto m = MAKE_TORCH_LIBRARY_IMPL(aten, $dispatch_key);
|
||
|
|
$dispatch_registrations_body
|
||
|
|
}"""
|
||
|
|
)
|
||
|
|
deferred_dispatch_registrations = deferred_template.substitute(
|
||
|
|
backend_name=backend_name,
|
||
|
|
dispatch_key=dispatch_key,
|
||
|
|
dispatch_registrations_body=dispatch_registrations_body,
|
||
|
|
)
|
||
|
|
|
||
|
|
fm.write_with_template(
|
||
|
|
f"Register{dispatch_key}.cpp",
|
||
|
|
"RegisterDispatchKey.cpp",
|
||
|
|
lambda: {
|
||
|
|
"extra_cuda_headers": "",
|
||
|
|
"external_backend_headers": external_backend_headers_str,
|
||
|
|
"ops_headers": "#include <ATen/Functions.h>"
|
||
|
|
if not per_operator_headers
|
||
|
|
else "",
|
||
|
|
"DispatchKey": dispatch_key,
|
||
|
|
"dispatch_namespace": dispatch_key.lower(),
|
||
|
|
"dispatch_headers": dest.gen_registration_headers(
|
||
|
|
backend_index, per_operator_headers=per_operator_headers, rocm=False
|
||
|
|
),
|
||
|
|
"dispatch_definitions": fm.substitute_with_template(
|
||
|
|
"RegisterDispatchDefinitions.ini",
|
||
|
|
lambda: {
|
||
|
|
"ns_prologue": ns_helper.prologue,
|
||
|
|
"ns_epilogue": ns_helper.epilogue,
|
||
|
|
"static_init_dispatch_registrations": static_init_dispatch_registrations,
|
||
|
|
"deferred_dispatch_registrations": deferred_dispatch_registrations,
|
||
|
|
"dispatch_helpers": dest.gen_registration_helpers(backend_index),
|
||
|
|
"dispatch_namespace": dispatch_key.lower(),
|
||
|
|
"dispatch_namespaced_definitions": "",
|
||
|
|
"dispatch_anonymous_definitions": list(
|
||
|
|
concatMap(
|
||
|
|
dest.RegisterDispatchKey(
|
||
|
|
backend_index,
|
||
|
|
Target.ANONYMOUS_DEFINITION,
|
||
|
|
selector,
|
||
|
|
rocm=False,
|
||
|
|
symint=True,
|
||
|
|
class_method_name=f"{class_name}",
|
||
|
|
skip_dispatcher_op_registration=False,
|
||
|
|
),
|
||
|
|
grouped_native_functions,
|
||
|
|
)
|
||
|
|
),
|
||
|
|
},
|
||
|
|
).split(newline),
|
||
|
|
},
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def run(
|
||
|
|
source_yaml: str, output_dir: str, dry_run: bool, impl_path: Optional[str] = None
|
||
|
|
) -> None:
|
||
|
|
# Assumes that this file lives at PYTORCH_ROOT/torchgen/gen_backend_stubs.py
|
||
|
|
pytorch_root = pathlib.Path(__file__).parent.parent.absolute()
|
||
|
|
template_dir = os.path.join(pytorch_root, "aten/src/ATen/templates")
|
||
|
|
|
||
|
|
def make_file_manager(install_dir: str) -> FileManager:
|
||
|
|
return FileManager(
|
||
|
|
install_dir=install_dir, template_dir=template_dir, dry_run=dry_run
|
||
|
|
)
|
||
|
|
|
||
|
|
fm = make_file_manager(output_dir)
|
||
|
|
|
||
|
|
native_yaml_path = os.path.join(
|
||
|
|
pytorch_root, "aten/src/ATen/native/native_functions.yaml"
|
||
|
|
)
|
||
|
|
tags_yaml_path = os.path.join(pytorch_root, "aten/src/ATen/native/tags.yaml")
|
||
|
|
parsed_yaml = parse_native_yaml(native_yaml_path, tags_yaml_path)
|
||
|
|
native_functions, backend_indices = (
|
||
|
|
parsed_yaml.native_functions,
|
||
|
|
parsed_yaml.backend_indices,
|
||
|
|
)
|
||
|
|
grouped_native_functions = get_grouped_native_functions(native_functions)
|
||
|
|
parsed_backend_yaml = parse_backend_yaml(
|
||
|
|
source_yaml, grouped_native_functions, backend_indices
|
||
|
|
)
|
||
|
|
backend_key = parsed_backend_yaml.backend_key
|
||
|
|
autograd_key = parsed_backend_yaml.autograd_key
|
||
|
|
cpp_namespace = parsed_backend_yaml.cpp_namespace
|
||
|
|
class_name = parsed_backend_yaml.class_name
|
||
|
|
backend_indices = parsed_backend_yaml.backend_indices
|
||
|
|
|
||
|
|
selector = SelectiveBuilder.get_nop_selector()
|
||
|
|
|
||
|
|
if backend_key is None:
|
||
|
|
# This could be useful if a backend wants to quickly set up a noop yaml file but doesn't have any kernels ready yet.
|
||
|
|
return
|
||
|
|
|
||
|
|
if class_name is None:
|
||
|
|
# class_name is an optional argument to backend yaml file.
|
||
|
|
# if specified it allows an external backend to override
|
||
|
|
# the name of the class that all generated kernel definitions live under.
|
||
|
|
# if not specified, its value is given as native_function_class_name.
|
||
|
|
class_name = backend_indices[backend_key].native_function_class_name()
|
||
|
|
assert class_name is not None
|
||
|
|
|
||
|
|
if impl_path is not None:
|
||
|
|
error_on_missing_kernels(
|
||
|
|
native_functions,
|
||
|
|
backend_indices,
|
||
|
|
backend_key,
|
||
|
|
autograd_key,
|
||
|
|
class_name,
|
||
|
|
impl_path,
|
||
|
|
)
|
||
|
|
|
||
|
|
gen_dispatchkey_nativefunc_headers(
|
||
|
|
fm,
|
||
|
|
class_name,
|
||
|
|
cpp_namespace,
|
||
|
|
backend_indices,
|
||
|
|
grouped_native_functions,
|
||
|
|
backend_key,
|
||
|
|
autograd_key,
|
||
|
|
)
|
||
|
|
|
||
|
|
for dispatch_key in (
|
||
|
|
[backend_key] if autograd_key is None else [backend_key, autograd_key]
|
||
|
|
):
|
||
|
|
gen_dispatcher_registrations(
|
||
|
|
fm,
|
||
|
|
output_dir,
|
||
|
|
class_name,
|
||
|
|
backend_indices,
|
||
|
|
grouped_native_functions,
|
||
|
|
backend_key,
|
||
|
|
dispatch_key,
|
||
|
|
selector,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|