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
|
import argparse
import itertools
import os
from typing import Sequence, TypeVar, Union
from libfb.py.log import set_simple_logging # type: ignore[import]
from torchgen import gen
from torchgen.context import native_function_manager
from torchgen.model import DispatchKey, NativeFunctionsGroup, NativeFunctionsViewGroup
from torchgen.static_runtime import config, generator
# Given a list of `grouped_native_functions` sorted by their op names, return a list of
# lists each of which groups ops that share the base name. For example, `mean` and
# `mean.dim` are grouped together by this function.
NativeGroupT = TypeVar(
"NativeGroupT",
bound=Union[NativeFunctionsGroup, NativeFunctionsViewGroup],
)
def group_functions_by_op_name(
grouped_native_functions: Sequence[NativeGroupT],
) -> Sequence[Sequence[NativeGroupT]]:
if not grouped_native_functions:
return []
groups = []
def is_supported(g: Union[NativeFunctionsGroup, NativeFunctionsViewGroup]) -> bool:
with native_function_manager(g):
return generator.is_supported(g)
eligible_ops = (g for g in grouped_native_functions if is_supported(g))
groups = [
list(group)
for k, group in (
itertools.groupby(
eligible_ops,
key=lambda g: config.func_name_base_str(g),
)
)
]
return groups
def clang_format(cpp_file_path: str) -> None:
import subprocess
subprocess.run(["clang-format", "-i", cpp_file_path])
def write_cpp(cpp_ops: Sequence[str], file_path: str) -> None:
code = "\n".join(cpp_ops)
generated = f"""// @lint-ignore-every CLANGTIDY HOWTOEVEN
// AUTO-GENERATED FROM: torchgen/static_runtime/gen_static_runtime_ops.py
#include <torch/csrc/jit/runtime/static/ops.h>
#include <ATen/CPUFunctions.h>
#include <ATen/InferSize.h>
#include <ATen/NativeFunctions.h>
#include <ATen/Parallel.h>
#include <ATen/ScalarOps.h>
#include <ATen/TensorUtils.h>
#include <ATen/cpu/vec/functional.h>
#include <ATen/cpu/vec/vec.h>
#include <ATen/native/EmbeddingBag.h>
#include <ATen/native/Fill.h>
#include <ATen/native/IndexingUtils.h>
#include <ATen/native/Resize.h>
#include <ATen/native/SharedReduceOps.h>
#include <ATen/native/TensorAdvancedIndexing.h>
#include <ATen/native/cpu/SerialStackImpl.h>
#include <ATen/native/layer_norm.h>
#include <ATen/native/quantized/cpu/fbgemm_utils.h>
#include <ATen/native/quantized/cpu/qembeddingbag.h>
#include <ATen/native/quantized/cpu/qembeddingbag_prepack.h>
#include <ATen/quantized/QTensorImpl.h>
#include <ATen/quantized/Quantizer.h>
#include <c10/core/ScalarType.h>
#include <c10/core/WrapDimMinimal.h>
#include <c10/util/irange.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/runtime/static/impl.h>
#include <torch/csrc/jit/runtime/static/te_wrapper.h>
#include <torch/csrc/jit/runtime/vararg_functions.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
#include <torch/csrc/jit/tensorexpr/llvm_codegen.h>
#include <torch/csrc/jit/tensorexpr/loopnest.h>
namespace torch {{
namespace jit {{
{code}
}} // namespace jit
}} // namespace torch
"""
with open(file_path, "w") as f:
f.write(generated)
clang_format(file_path)
def write_test_cpp(cpp_ops: Sequence[str], file_path: str) -> None:
code = "\n".join(cpp_ops)
generated = f"""// @lint-ignore-every CLANGTIDY HOWTOEVEN
// AUTO-GENERATED FROM: torchgen/static_runtime/gen_static_runtime_ops.py
#include <gtest/gtest.h>
#include <torch/csrc/jit/runtime/static/impl.h>
#include <torch/torch.h>
#include "test_utils.h"
using namespace caffe2;
using namespace torch;
using namespace torch::jit;
using namespace torch::jit::test;
using c10::IValue;
{code}
"""
with open(file_path, "w") as f:
f.write(generated)
clang_format(file_path)
def main() -> None:
parser = argparse.ArgumentParser(description="Generate ATen source files")
parser.add_argument(
"-s",
"--source-path",
help="path to source directory for ATen",
default="caffe2/aten/src/ATen",
)
parser.add_argument(
"-p",
"--generated-ops-cpp-path",
help="path to directory to generate op dispatcher .cpp file",
default="caffe2/torch/csrc/jit/runtime/static/generated_ops.cpp",
)
parser.add_argument(
"-t",
"--generated-ops-test-cpp-path",
help="path to directory to generate op dispatcher .cpp file",
default="caffe2/benchmarks/static_runtime/test_generated_ops.cc",
)
options = parser.parse_args()
native_yaml_path = os.path.join(options.source_path, "native/native_functions.yaml")
tags_yaml_path = os.path.join(options.source_path, "native/tags.yaml")
parsed_yaml = gen.parse_native_yaml(native_yaml_path, tags_yaml_path)
native_functions, backend_indices = (
parsed_yaml.native_functions,
parsed_yaml.backend_indices,
)
op_generator = generator.GenOpDispatcher()
test_case_generator = generator.GenOpTestCase()
native_functions_groups = [
g
for g in gen.get_grouped_native_functions(native_functions)
if isinstance(g, NativeFunctionsGroup)
]
supported_functions_groups = group_functions_by_op_name(native_functions_groups)
out_variant_op_result = [
op_generator.out_variant(groups, backend_indices[DispatchKey.CPU])
for groups in supported_functions_groups
]
out_variant_test_result = [
test_case_generator.out_variant(groups) for groups in supported_functions_groups
]
native_functions_view_groups = [
g
for g in gen.get_grouped_by_view_native_functions(native_functions)
if isinstance(g, NativeFunctionsViewGroup)
]
supported_functions_view_groups = group_functions_by_op_name(
native_functions_view_groups
)
view_op_result = [
op_generator.view(groups, backend_indices[DispatchKey.CPU])
for groups in supported_functions_view_groups
]
view_test_result = [
test_case_generator.view(groups) for groups in supported_functions_view_groups
]
op_result = out_variant_op_result + ["\n\n"] + view_op_result
test_result = out_variant_test_result + ["\n\n"] + view_test_result
write_cpp(op_result, options.generated_ops_cpp_path)
write_test_cpp(test_result, options.generated_ops_test_cpp_path)
print(
"\ntotal grouped native ops: %d"
% len(gen.get_grouped_native_functions(native_functions))
)
print("grouped native ops with out variant: %d" % len(native_functions_groups))
supported_functions_num = sum(
[len(groups) for groups in supported_functions_groups]
)
print("generated functions groups with out variant: %d" % supported_functions_num)
print("\nview grouped native ops: %d" % len(native_functions_view_groups))
supported_view_functions_num = sum(
[len(groups) for groups in supported_functions_view_groups]
)
print("generated functions view groups: %d" % supported_view_functions_num)
print(
"\noverall generated : %d"
% (supported_functions_num + supported_view_functions_num)
)
if __name__ == "__main__":
set_simple_logging(escape_newlines=False)
main()
|