File size: 37,212 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 |
from __future__ import annotations
import argparse
import os
from collections import defaultdict
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Callable, TextIO, TYPE_CHECKING
import yaml
# Parse native_functions.yaml into a sequence of NativeFunctions and Backend Indices.
from torchgen import dest
from torchgen.api import cpp as aten_cpp
from torchgen.api.types import CppSignature, CppSignatureGroup, CType, NamedCType
from torchgen.context import (
method_with_native_function,
method_with_nested_native_function,
with_native_function_and_index,
)
from torchgen.executorch.api import et_cpp
from torchgen.executorch.api.custom_ops import (
ComputeNativeFunctionStub,
gen_custom_ops_registration,
)
from torchgen.executorch.api.types import contextArg, ExecutorchCppSignature
from torchgen.executorch.api.unboxing import Unboxing
from torchgen.executorch.model import ETKernelIndex, ETKernelKey, ETParsedYaml
from torchgen.executorch.parse import ET_FIELDS, parse_et_yaml, parse_et_yaml_struct
from torchgen.gen import (
get_custom_build_selector,
get_native_function_declarations,
get_native_function_declarations_from_ns_grouped_kernels,
get_native_function_schema_registrations,
LineLoader,
parse_native_yaml,
)
from torchgen.model import (
BackendIndex,
BackendMetadata,
DEFAULT_KERNEL_NAMESPACE,
DispatchKey,
FunctionSchema,
Location,
NativeFunction,
NativeFunctionsGroup,
OperatorName,
Variant,
)
from torchgen.utils import (
context,
FileManager,
make_file_manager,
mapMaybe,
NamespaceHelper,
)
if TYPE_CHECKING:
from collections.abc import Sequence
from torchgen.selective_build.selector import SelectiveBuilder
def _sig_decl_wrapper(sig: CppSignature | ExecutorchCppSignature) -> str:
"""
A wrapper function to basically get `sig.decl(include_context=True)`.
For ATen kernel, the codegen has no idea about ET contextArg, so we
use this wrapper to add it.
"""
if isinstance(sig, ExecutorchCppSignature):
return sig.decl()
returns_type = aten_cpp.returns_type(sig.func.returns).cpp_type()
cpp_args = [a.decl() for a in sig.arguments()]
cpp_args_str = ", ".join([contextArg.decl()] + cpp_args)
sig_decl = f"{returns_type} {sig.name()}({cpp_args_str})"
return sig_decl
def static_dispatch(
sig: CppSignature | ExecutorchCppSignature,
f: NativeFunction,
backend_indices: list[BackendIndex],
) -> str:
"""
For a given `NativeFunction`, find out the corresponding native function and dispatch to it. If zero or more than one
native function exists, error out. A simplified version of register_dispatch_key.py
Arguments:
sig: A CppSignature for this native function we want to use.
f: NativeFunction to generate static dispatch.
backend_indices: All available backends.
Return:
C++ code to call backend-specific functions, e.g., "return at::native::add(self, other, scale);"
"""
if len(backend_indices) == 0 or f.manual_kernel_registration:
return ""
backends = [b for b in backend_indices if b.has_kernel(f)]
static_block = None
if len(backends) == 1:
backend_metadata = backends[0].get_kernel(f)
if backend_metadata:
args = ", ".join(a.name for a in sig.arguments())
# Here we are assuming there's no difference between CppSignature and NativeSignature for Executorch.
static_block = f"return ::{backend_metadata.cpp_namespace}::{backend_metadata.kernel}({args});"
else:
static_block = f"""
ET_ASSERT_UNREACHABLE_MSG("The number of native function(s) binding to {f.func.name} is {len(backends)}.");
"""
return f"""
// {f.namespace}::{f.func}
TORCH_API inline {_sig_decl_wrapper(sig)} {{
{static_block}
}}
"""
# Generates Functions.h, which provides the functional public C++ API,
# and the scaffolding to call into the dispatcher from these functions.
@dataclass(frozen=True)
class ComputeFunction:
static_dispatch_backend_indices: list[BackendIndex]
selector: SelectiveBuilder
use_aten_lib: bool
is_custom_op: Callable[[NativeFunction], bool]
@method_with_native_function
def __call__(self, f: NativeFunction) -> str | None:
is_method_variant = False
if not self.selector.is_root_operator(f"{f.namespace}::{f.func.name}"):
return None
if Variant.function not in f.variants and Variant.method in f.variants:
is_method_variant = True
# only valid remaining case is only function is in f.variants
elif not (Variant.function in f.variants and Variant.method not in f.variants):
raise Exception( # noqa: TRY002
f"Can't handle native function {f.func} with the following variant specification {f.variants}."
)
sig: CppSignature | ExecutorchCppSignature = (
CppSignatureGroup.from_native_function(
f, method=False, fallback_binding=f.manual_cpp_binding
).most_faithful_signature()
if self.use_aten_lib
else ExecutorchCppSignature.from_native_function(f)
)
if self.use_aten_lib and not self.is_custom_op(f):
comma = ", "
if is_method_variant:
return f"""
// {f.namespace}::{f.func}
TORCH_API inline {_sig_decl_wrapper(sig)} {{
return {sig.arguments()[0].name}.{sig.name()}({comma.join(e.name for e in sig.arguments()[1:])});
}}
"""
else:
return f"""
// {f.namespace}::{f.func}
TORCH_API inline {_sig_decl_wrapper(sig)} {{
return at::{sig.name()}({comma.join(e.name for e in sig.arguments())});
}}
"""
else:
return static_dispatch(
sig,
f,
backend_indices=self.static_dispatch_backend_indices,
)
# Generates RegisterCodegenUnboxedKernels.cpp.
@dataclass(frozen=True)
class ComputeCodegenUnboxedKernels:
selector: SelectiveBuilder
use_aten_lib: bool
add_exception_boundary: bool
@method_with_nested_native_function
def __call__(
self,
unbox_kernel_entry: tuple[NativeFunction, tuple[ETKernelKey, BackendMetadata]],
) -> str:
f: NativeFunction = unbox_kernel_entry[0]
kernel_key: ETKernelKey | list[ETKernelKey] = unbox_kernel_entry[1][0]
kernel_meta: BackendMetadata = unbox_kernel_entry[1][1]
op_name = f"{f.namespace}::{f.func.name}"
if not self.selector.is_root_operator(op_name):
return ""
if not isinstance(kernel_key, list):
kernel_key = [kernel_key]
used_kernel_keys = self.selector.et_get_selected_kernels(
op_name, [k.to_native_string() for k in kernel_key]
)
if not used_kernel_keys:
return ""
sig: CppSignature | ExecutorchCppSignature
argument_type_gen: Callable[..., NamedCType]
return_type_gen: Callable[..., CType]
if self.use_aten_lib:
sig = CppSignatureGroup.from_native_function(
f, method=False, fallback_binding=f.manual_cpp_binding
).most_faithful_signature()
argument_type_gen = aten_cpp.argumenttype_type
return_type_gen = aten_cpp.returns_type
arguments = sig.arguments()
kernel_call = f"torch::executor::{f.namespace}::{sig.name()}"
else:
sig = ExecutorchCppSignature.from_native_function(f)
argument_type_gen = et_cpp.argumenttype_type
return_type_gen = et_cpp.returns_type
arguments = sig.arguments(include_context=False)
kernel_call = f"{kernel_meta.cpp_namespace}::{kernel_meta.kernel}"
# parse arguments into C++ code
binding_list, code_list = Unboxing(
argument_type_gen=argument_type_gen
).convert_arguments(arguments)
# for each C++ argument, generate the conversion code
code_connector = "\n\t"
arg_connector = ", "
args_str = f"{arg_connector.join(e.name for e in binding_list)}"
event_tracer_output_logging = ""
output_ids = []
if len(f.func.returns) == 0:
if len(f.func.arguments.out) == 0:
raise Exception( # noqa: TRY002
f"Can't handle native function {f.func} with no returns and no out yet."
)
out = f.func.arguments.out[0]
return_assignment = f"""stack[{len(binding_list)}] = &{out.name};"""
ret_prefix = ""
output_ids = [len(binding_list)]
else:
if len(f.func.arguments.out) == 0:
return_assignment = (
f"""*stack[{len(binding_list)}] = EValue(result_);"""
)
ret_prefix = return_type_gen(f.func.returns).cpp_type() + " result_ = "
output_ids = [len(binding_list)]
else:
return_assignment = ""
ret_prefix = ""
output_ids = [
len(binding_list) - (i + 1)
for i in reversed(range(len(f.func.arguments.out)))
]
for output_id in output_ids:
event_tracer_output_logging += (
f"internal::event_tracer_log_evalue("
f"context.internal_event_tracer(), "
f"*stack[{output_id}]);\n"
)
exception_boundary_begin = ""
exception_boundary_end = ""
if self.add_exception_boundary:
indent = " " * 8
exception_boundary_begin = indent + "try {"
exception_boundary_end = f"""{indent}}} catch (const std::exception& ex) {{
{indent} ET_LOG(Error, "Kernel threw an exception: %s", ex.what());
{indent} context.fail(torch::executor::Error::Internal);
{indent}}}"""
newline = "\n "
return "\n".join(
[
f"""
Kernel(
"{f.namespace}::{f.func.name}",{newline + '"' + (k + '",') if k != "default" else ""}
[]({contextArg.defn()}, EValue** stack) {{
{code_connector.join(code_list)}
{exception_boundary_begin}
internal::EventTracerProfileOpScope event_tracer_op_scope(context.internal_event_tracer(), "native_call_{f.func.name}");
EXECUTORCH_SCOPE_PROF("native_call_{f.func.name}");
{ret_prefix}{kernel_call}(context, {args_str});
{event_tracer_output_logging}
{return_assignment}
{exception_boundary_end}
}}
),
"""
for k in used_kernel_keys
]
)
def gen_unboxing(
*,
native_functions: Sequence[NativeFunction],
cpu_fm: FileManager,
selector: SelectiveBuilder,
use_aten_lib: bool,
kernel_index: ETKernelIndex,
manual_registration: bool,
add_exception_boundary: bool = False,
) -> None:
# Iterable type for write_sharded is a Tuple of (native_function, (kernel_key, metadata))
def key_func(
item: tuple[NativeFunction, tuple[ETKernelKey, BackendMetadata]],
) -> str:
return item[0].root_name + ":" + item[1][0].to_native_string()
items: list[tuple[NativeFunction, tuple[ETKernelKey, BackendMetadata]]] = [
(native_function, (kernel_key, metadata))
for native_function in native_functions
for kernel_key, metadata in kernel_index.get_kernels(native_function).items()
]
header = ["Functions.h" if use_aten_lib else "NativeFunctions.h"]
filename = (
"RegisterKernels.cpp"
if manual_registration
else "RegisterCodegenUnboxedKernels.cpp"
)
cpu_fm.write_sharded(
filename,
items,
key_fn=key_func,
env_callable=lambda unbox_kernel_entry: {
"unboxed_kernels": [
ComputeCodegenUnboxedKernels(
selector, use_aten_lib, add_exception_boundary
)(unbox_kernel_entry)
],
"fn_header": header
if unbox_kernel_entry == items[0]
else [], # Only write header once
},
num_shards=1,
sharded_keys={"unboxed_kernels", "fn_header"},
)
@with_native_function_and_index # type: ignore[arg-type]
def compute_native_function_declaration(
g: NativeFunctionsGroup | NativeFunction, kernel_index: ETKernelIndex
) -> list[str]:
assert isinstance(g, NativeFunction)
sig = ExecutorchCppSignature.from_native_function(f=g)
metadata_list = kernel_index.get_kernels(g).values()
if metadata_list is None:
return []
# for kernels in lean mode, we declare two versions, one with context and one without.
# In the end we will cleanup the unused one.
def gen_decl(metadata: BackendMetadata, include_context: bool) -> str:
return f"{sig.decl(name=metadata.kernel, include_context=include_context)};"
return [
gen_decl(metadata, include_context)
for include_context in [False, True]
for metadata in metadata_list
]
def gen_functions_declarations(
*,
native_functions: Sequence[NativeFunction],
kernel_index: ETKernelIndex,
selector: SelectiveBuilder,
use_aten_lib: bool,
custom_ops_native_functions: Sequence[NativeFunction] | None = None,
) -> str:
"""
Generates namespace separated C++ function API inline declaration/definitions.
Native functions are grouped by namespaces and the generated code is wrapped inside
namespace blocks.
E.g., for `custom_1::foo.out` in yaml file we will generate a C++ API as a symbol
in `torch::executor::custom_1::foo_out`. This way we avoid symbol conflict when
the other `custom_2::foo.out` is available.
"""
# convert kernel index to BackendIndex. This is because we can't handle ETKernelIndex yet.
# TODO larryliu: evaluate if this code is still needed. If yes let it handle ETKernelIndex.
backend_index = kernel_index._to_backend_index()
ns_grouped_functions = defaultdict(list)
for native_function in native_functions:
ns_grouped_functions[native_function.namespace].append(native_function)
functions_declarations = ""
newline = "\n"
for namespace in ns_grouped_functions:
ns_helper = NamespaceHelper(
namespace_str=namespace,
entity_name="",
max_level=3,
)
declarations = list(
mapMaybe(
ComputeFunction(
static_dispatch_backend_indices=[backend_index],
selector=selector,
use_aten_lib=use_aten_lib,
is_custom_op=lambda f: custom_ops_native_functions is not None
and f in custom_ops_native_functions,
),
ns_grouped_functions[namespace],
)
)
functions_declarations += f"""
{ns_helper.prologue}
{newline.join(declarations)}
{ns_helper.epilogue}
"""
return functions_declarations
def get_ns_grouped_kernels(
*,
native_functions: Sequence[NativeFunction],
kernel_index: ETKernelIndex,
native_function_decl_gen: Callable[
[
NativeFunctionsGroup | NativeFunction,
ETKernelIndex,
],
list[str],
],
) -> dict[str, list[str]]:
ns_grouped_kernels: dict[str, list[str]] = defaultdict(list)
for f in native_functions:
native_function_namespaces = set()
op_kernels = kernel_index.get_kernels(f)
for backend_metadata in op_kernels.values():
if backend_metadata:
namespace = backend_metadata.cpp_namespace
native_function_namespaces.add(namespace)
else:
namespace = DEFAULT_KERNEL_NAMESPACE
assert len(native_function_namespaces) <= 1, (
f"Codegen only supports one namespace per operator, got {native_function_namespaces}"
)
ns_grouped_kernels[namespace].extend(
native_function_decl_gen(f, kernel_index)
)
return ns_grouped_kernels
def gen_headers(
*,
native_functions: Sequence[NativeFunction],
gen_custom_ops_header: bool,
custom_ops_native_functions: Sequence[NativeFunction],
selector: SelectiveBuilder,
kernel_index: ETKernelIndex,
cpu_fm: FileManager,
use_aten_lib: bool,
) -> None:
"""Generate headers.
Args:
native_functions (Sequence[NativeFunction]): a collection of NativeFunction for ATen ops.
gen_custom_ops_header (bool): whether we should generate CustomOpsNativeFunctions.h
custom_ops_native_functions (Sequence[NativeFunction]): a collection of NativeFunction for custom ops.
kernel_index (ETKernelIndex): kernel collection
cpu_fm (FileManager): file manager manages output stream
use_aten_lib (bool): whether we are generating for PyTorch types or Executorch types.
"""
aten_headers = ["#include <ATen/Functions.h>"]
backend_indices = {DispatchKey.CPU: kernel_index._to_backend_index()}
if gen_custom_ops_header:
cpu_fm.write_with_template(
"CustomOpsNativeFunctions.h",
"NativeFunctions.h",
lambda: {
"nativeFunctions_declarations": get_native_function_declarations(
grouped_native_functions=custom_ops_native_functions,
backend_indices=backend_indices,
native_function_decl_gen=dest.compute_native_function_declaration,
),
"headers": [
"#include <ATen/ATen.h>",
"#include <torch/torch.h>",
],
},
)
aten_headers.append('#include "CustomOpsNativeFunctions.h"')
cpu_fm.write(
"Functions.h",
lambda: {
"static_dispatch_extra_headers": aten_headers
if use_aten_lib
else ['#include "NativeFunctions.h"'],
"Functions_declarations": gen_functions_declarations(
native_functions=native_functions,
kernel_index=kernel_index,
selector=selector,
use_aten_lib=use_aten_lib,
custom_ops_native_functions=custom_ops_native_functions,
),
},
)
cpu_fm.write(
"RegisterKernels.h",
lambda: {
"generated_comment": "@" + "generated by torchgen/gen_executorch.py",
},
)
headers = {
"headers": [
"#include <executorch/runtime/core/exec_aten/exec_aten.h> // at::Tensor etc.",
"#include <executorch/runtime/kernel/kernel_runtime_context.h>",
],
}
if use_aten_lib:
headers["headers"].append("#include <executorch/codegen/macros.h> // TORCH_API")
cpu_fm.write(
"NativeFunctions.h",
lambda: dict(
{
"nativeFunctions_declarations": get_native_function_declarations(
grouped_native_functions=native_functions,
backend_indices=backend_indices,
native_function_decl_gen=dest.compute_native_function_declaration,
),
},
**headers,
),
)
else:
ns_grouped_kernels = get_ns_grouped_kernels(
native_functions=native_functions,
kernel_index=kernel_index,
native_function_decl_gen=compute_native_function_declaration, # type: ignore[arg-type]
)
cpu_fm.write(
"NativeFunctions.h",
lambda: dict(
{
"nativeFunctions_declarations": get_native_function_declarations_from_ns_grouped_kernels(
ns_grouped_kernels=ns_grouped_kernels,
),
},
**headers,
),
)
def gen_custom_ops(
*,
native_functions: Sequence[NativeFunction],
selector: SelectiveBuilder,
kernel_index: ETKernelIndex,
cpu_fm: FileManager,
rocm: bool,
) -> None:
dispatch_key = DispatchKey.CPU
(
anonymous_definition,
static_init_dispatch_registrations,
) = gen_custom_ops_registration(
native_functions=native_functions,
selector=selector,
kernel_index=kernel_index,
rocm=rocm,
)
cpu_fm.write_with_template(
f"Register{dispatch_key}CustomOps.cpp",
"RegisterDispatchKeyCustomOps.cpp",
lambda: {
"ops_headers": '#include "CustomOpsNativeFunctions.h"',
"DispatchKey": dispatch_key,
"dispatch_namespace": dispatch_key.lower(),
"dispatch_namespaced_definitions": "",
"dispatch_anonymous_definitions": anonymous_definition,
"static_init_dispatch_registrations": static_init_dispatch_registrations,
},
)
cpu_fm.write_with_template(
f"Register{dispatch_key}Stub.cpp",
"RegisterDispatchKeyCustomOps.cpp",
lambda: {
"ops_headers": "",
"DispatchKey": dispatch_key,
"dispatch_namespace": dispatch_key.lower(),
"dispatch_namespaced_definitions": "",
"dispatch_anonymous_definitions": list(
mapMaybe(ComputeNativeFunctionStub(), native_functions)
),
"static_init_dispatch_registrations": static_init_dispatch_registrations,
},
)
(
aten_schema_registrations,
schema_registrations,
) = get_native_function_schema_registrations(
native_functions=native_functions,
schema_selector=selector,
)
cpu_fm.write(
"RegisterSchema.cpp",
lambda: {
"schema_registrations": schema_registrations,
"aten_schema_registrations": aten_schema_registrations,
},
)
def translate_native_yaml(
tags_yaml_path: str,
aten_yaml_path: str,
native_yaml_path: str | None,
use_aten_lib: bool,
out_file: TextIO,
) -> None:
"""Translates Executorch DSL dialect to use the same syntax as
native_functions.yaml. The major difference is that Executorch DSL dialect
supports "op" key, where it refers to the operator name in native_functions.yaml.
For example, a functions.yaml may have the following entry:
- op: add.out
...
It needs to be translated to the following:
- func: add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
...
We go in aten_yaml_path and find the operator schema for "add.out" and add it
to the original functions.yaml. We also add required field "variants", where for
Executorch it will always be "function".
For ATen mode we don't have to do the translation because native_yaml_path is
the same as native_functions.yaml.
Args:
tags_yaml_path: Path to a tags.yaml file to satisfy codegen parsing.
It is not optional.
aten_yaml_path: Path to ATen operator yaml file native_functions.yaml.
native_yaml_path: Path to a functions.yaml file to parse.
If the path does not exist in the filesystem, it is treated as an
empty file. If `custom_ops_yaml_path` exists, the contents of that
file are appended to the yaml input to be parsed.
use_aten_lib: We use this flag to determine if we want to generate native
functions. In ATen mode we should generate out= variants.
out_file: The IO object that we are writing into.
Returns:
None
"""
if use_aten_lib:
with open(aten_yaml_path) as aten_yaml:
out_file.writelines(aten_yaml.readlines())
return
native_functions, persisted_fields = parse_et_yaml(
aten_yaml_path,
tags_yaml_path,
None,
skip_native_fns_gen=False,
)
func_to_scoped_name: dict[FunctionSchema, str] = {
f.func: f"{f.namespace}::{f.func.name}" for f in native_functions
}
op_to_scoped_name: dict[OperatorName, str] = {
func.name: name for func, name in func_to_scoped_name.items()
}
schema_dict = {name: str(func) for func, name in func_to_scoped_name.items()}
kernel_persist_dict: dict[str, dict[str, Any]] = {
op_to_scoped_name[op]: v for op, v in persisted_fields.items()
}
if (
not native_yaml_path
or not os.path.exists(native_yaml_path)
or os.stat(native_yaml_path).st_size == 0
):
return
with open(native_yaml_path) as native_yaml:
native_es = yaml.load(native_yaml, Loader=LineLoader)
if not native_es:
return
for e in native_es:
assert isinstance(e.get("__line__"), int), e
loc = Location(native_yaml_path, e.pop("__line__"))
with context(lambda: f"in {loc}:\n "):
if "variants" not in e:
e["variants"] = "function"
if "func" in e:
continue
assert isinstance(e.get("op"), str), e
opname = e.pop("op")
if "::" not in opname:
opname = "aten::" + opname
assert opname in schema_dict
e["func"] = schema_dict.get(opname)
# Write out persisted kernel information
if opname in kernel_persist_dict:
for k, v in kernel_persist_dict[opname].items():
e[k] = v
yaml.dump(native_es, out_file, width=1000)
def parse_yaml(
path: str | None,
tags_yaml_path: str,
function_filter: Callable[[NativeFunction], bool],
skip_native_fns_gen: bool = False,
) -> tuple[
list[NativeFunction],
dict[DispatchKey, dict[OperatorName, BackendMetadata]] | ETKernelIndex,
]:
if path and os.path.exists(path) and os.stat(path).st_size > 0:
with open(path) as f:
es = yaml.load(f, Loader=LineLoader)
# Check for kernel index structure
kernel_index = (
parse_et_yaml_struct(es) if any("kernels" in e for e in es) else None
)
# Remove ET specific fields from entries for BC compatibility
for entry in es:
for field in ET_FIELDS:
entry.pop(field, None)
parsed_yaml = parse_native_yaml(
path,
tags_yaml_path,
None,
skip_native_fns_gen=skip_native_fns_gen,
loaded_yaml=es,
)
native_functions = list(filter(function_filter, parsed_yaml.native_functions))
op_names = [f.func.name for f in native_functions]
# (1) Return ETKernelIndex if kernel index is present
if kernel_index is not None:
filtered_index = {
op_name: kernel_mapping
for op_name, kernel_mapping in kernel_index.index.items()
if op_name in op_names
}
return native_functions, ETKernelIndex(index=filtered_index)
# (2) Return BackendIndices if kernel index is absent
def map_index(
m: dict[OperatorName, BackendMetadata],
) -> dict[OperatorName, BackendMetadata]:
return {op: m[op] for op in m if op in op_names}
backend_indices = {
k: map_index(b.index) for (k, b) in parsed_yaml.backend_indices.items()
}
return native_functions, backend_indices
else:
return [], {}
def parse_yaml_files(
tags_yaml_path: str,
aten_yaml_path: str,
native_yaml_path: str | None,
custom_ops_yaml_path: str | None,
selector: SelectiveBuilder,
use_aten_lib: bool,
) -> tuple[ETParsedYaml, ETParsedYaml | None]:
"""Parses functions.yaml and custom_ops.yaml files.
Args:
tags_yaml_path: Path to a tags.yaml file to satisfy codegen parsing.
It is not optional.
aten_yaml_path: Path to ATen operator yaml file native_functions.yaml.
native_yaml_path: Path to a functions.yaml file to parse.
If the path does not exist in the filesystem, it is treated as an
empty file. If `custom_ops_yaml_path` exists, the contents of that
file are appended to the yaml input to be parsed.
custom_ops_yaml_path: Path to a custom_ops.yaml file to parse. If
the path does not exist in the filesystem, it is ignored.
selector: For selective build.
use_aten_lib: We use this flag to determine if we want to generate native
functions. In ATen mode we should generate out= variants.
Returns:
A tuple with two elements:
[0]: The parsed results of concatenating the contents of
`native_yaml_path` and `custom_ops_yaml_path`.
[1]: The parsed results of the contents of `custom_ops_yaml_path`, if
present. If not present, None.
"""
import tempfile
# only include selected ops, this is because we want to avoid
def function_filter(f: NativeFunction) -> bool:
return selector.is_native_function_selected(f)
with tempfile.TemporaryDirectory() as tmpdirname:
translated_yaml_path = os.path.join(tmpdirname, "translated.yaml")
with open(translated_yaml_path, "w") as translated:
translate_native_yaml(
tags_yaml_path,
aten_yaml_path,
native_yaml_path,
use_aten_lib,
translated,
)
translated_functions, translated_indices = parse_yaml(
translated_yaml_path, tags_yaml_path, function_filter, not use_aten_lib
)
custom_ops_functions, custom_ops_indices = parse_yaml(
custom_ops_yaml_path, tags_yaml_path, function_filter, True
)
# Convert BackendIndices to ETKernelIndex
if not isinstance(translated_indices, ETKernelIndex):
translated_indices = ETKernelIndex.from_backend_indices(translated_indices)
if not isinstance(custom_ops_indices, ETKernelIndex):
custom_ops_indices = ETKernelIndex.from_backend_indices(custom_ops_indices)
combined_functions = translated_functions + custom_ops_functions
combined_kernel_index = ETKernelIndex.merge_indices(
translated_indices, custom_ops_indices
)
combined_yaml = ETParsedYaml(combined_functions, combined_kernel_index)
custom_ops_parsed_yaml = ETParsedYaml(custom_ops_functions, custom_ops_indices)
return combined_yaml, custom_ops_parsed_yaml
def main() -> None:
parser = argparse.ArgumentParser(description="Generate operator source files")
# Although we don't refer to --source-path directly, make_file_manager()
# expects it to point to a directory that contains a templates/ subdirectory
# containing the file templates.
parser.add_argument(
"-s",
"--source-path",
help="path to source directory for kernel templates",
)
parser.add_argument(
"--functions-yaml-path",
"--functions_yaml_path",
help="path to the functions.yaml file to use. Optional, but at least "
"one of --functions-yaml-path and --custom-ops-yaml-path must be "
"specified.",
)
parser.add_argument(
"--custom-ops-yaml-path",
"--custom_ops_yaml_path",
help="path to the custom_ops.yaml file to use. Optional, but at least "
"one of --functions-yaml-path and --custom-ops-yaml-path must be "
"specified.",
)
parser.add_argument(
"--aten-yaml-path",
"--aten_yaml_path",
help="path to native_functions.yaml file.",
)
# Note that make_file_manager() also looks at --install-dir.
parser.add_argument(
"-d",
"--install-dir",
"--install_dir",
help="output directory",
default="build/generated",
)
parser.add_argument(
"-o",
"--output-dependencies",
help="output a list of dependencies into the given file and exit",
)
# Although we don't refer to --dry-run directly, make_file_manager() looks
# for it.
parser.add_argument(
"--dry-run",
action="store_true",
help="run without writing any files (still updates outputs)",
)
parser.add_argument(
"--static-dispatch-backend",
"--static_dispatch_backend",
nargs="*",
help="generate static dispatch code for the specific backend (if set)",
)
parser.add_argument(
"--op-registration-whitelist",
"--op_registration_whitelist",
nargs="*",
help="filter op registrations by the whitelist (if set); "
"each item is `namespace`::`operator name` without overload name; "
"e.g.: aten::empty aten::conv2d ...",
)
parser.add_argument(
"--op-selection-yaml-path",
"--op_selection_yaml_path",
help="Provide a path to the operator selection (for custom build) YAML "
"that contains the information about the set of selected operators "
"and their categories (training, ...). Each operator is either a "
"full operator name with overload or just a bare operator name. "
"The operator names also contain the namespace prefix (e.g. aten::)",
)
parser.add_argument(
"--tags-path",
help="Path to tags.yaml. Required by yaml parsing in codegen system.",
)
parser.add_argument(
"--rocm",
action="store_true",
help="reinterpret CUDA as ROCm/HIP and adjust filepaths accordingly",
)
parser.add_argument(
"--use-aten-lib",
"--use_aten_lib",
action="store_true",
help="a boolean flag to indicate whether we use ATen kernels or not, in the future this flag will be per "
"operator",
)
parser.add_argument(
"--manual_registration",
"--manual-registration",
action="store_true",
help="a boolean flag to indicate whether we want to manually call"
"register_kernels() or rely on static init. ",
)
parser.add_argument(
"--generate",
type=str,
nargs="*",
choices=["headers", "sources"],
default=["headers", "sources"],
help="Generate only a subset of files",
)
parser.add_argument(
"--add-exception-boundary",
"--add_exception_boundary",
action="store_true",
help="whether to add a try/catch in the generated kernel wrapper to "
"convert exceptions to clean failures.",
)
options = parser.parse_args()
assert options.tags_path, "tags.yaml is required by codegen yaml parsing."
selector = get_custom_build_selector(
options.op_registration_whitelist,
options.op_selection_yaml_path,
)
parsed_yaml, custom_ops_parsed_yaml = parse_yaml_files(
aten_yaml_path=options.aten_yaml_path,
tags_yaml_path=options.tags_path,
native_yaml_path=options.functions_yaml_path,
custom_ops_yaml_path=options.custom_ops_yaml_path,
selector=selector,
use_aten_lib=options.use_aten_lib,
)
native_functions, kernel_index = (
parsed_yaml.native_functions,
parsed_yaml.kernel_index,
)
custom_ops_native_functions = (
custom_ops_parsed_yaml.native_functions if custom_ops_parsed_yaml else []
)
cpu_fm = make_file_manager(options=options)
if "headers" in options.generate:
# generate CustomOpsNativeFunctions.h when custom_ops.yaml is present, to match the build system.
gen_headers(
native_functions=native_functions,
gen_custom_ops_header=options.custom_ops_yaml_path,
custom_ops_native_functions=custom_ops_native_functions,
selector=selector,
kernel_index=kernel_index,
cpu_fm=cpu_fm,
use_aten_lib=options.use_aten_lib,
)
if "sources" in options.generate:
gen_unboxing(
native_functions=native_functions,
cpu_fm=cpu_fm,
selector=selector,
use_aten_lib=options.use_aten_lib,
kernel_index=kernel_index,
manual_registration=options.manual_registration,
add_exception_boundary=options.add_exception_boundary,
)
if custom_ops_native_functions:
gen_custom_ops(
native_functions=custom_ops_native_functions,
selector=selector,
kernel_index=kernel_index,
cpu_fm=cpu_fm,
rocm=options.rocm,
)
if options.output_dependencies:
depfile_path = Path(options.output_dependencies).resolve()
depfile_name = depfile_path.name
depfile_stem = depfile_path.stem
for fm, prefix in [
(cpu_fm, ""),
]:
varname = prefix + depfile_stem
path = depfile_path.parent / (prefix + depfile_name)
fm.write_outputs(varname, str(path))
if __name__ == "__main__":
main()
|