mirror of
https://github.com/zebrajr/pytorch.git
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eebe0ee141
Summary: keys and change codegen to take ETKernelIndex We are adding support for dtype and dim order specialized kernel registration. This requires us to reorganize `BackendIndex` (which is a `Dict[DispatchKey, Dict[OperatorName, BackendMetadata]]`) to be `Dict[OperatorName, Dict[ETKernelKey, BackendMetadata]]`. This PR adds new data structures in order to support this change: * `ETKernelKey` to retrieve a certain kernel from the registry. * `ETKernelIndex`, the dictionary from operator name to kernel key to kernel mapping. Note that the codegen logic is not changed yet, we need subsequent diffs to actually generate code for different kernel keys. Test Plan: Added tests Reviewed By: Jack-Khuu Differential Revision: D46407096 Pull Request resolved: https://github.com/pytorch/pytorch/pull/102874 Approved by: https://github.com/Jack-Khuu, https://github.com/kirklandsign
834 lines
30 KiB
Python
834 lines
30 KiB
Python
import argparse
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import os
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import pathlib
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from collections import defaultdict
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from dataclasses import dataclass
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from typing import Callable, Dict, List, Optional, Sequence, TextIO, Tuple, Union
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import yaml
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# Parse native_functions.yaml into a sequence of NativeFunctions and Backend Indices.
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from torchgen import dest
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from torchgen.api import cpp as aten_cpp
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from torchgen.api.types import CppSignature, CppSignatureGroup, CType, NamedCType
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from torchgen.context import method_with_native_function, with_native_function_and_index
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from torchgen.executorch.api import et_cpp
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from torchgen.executorch.api.custom_ops import (
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ComputeNativeFunctionStub,
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gen_custom_ops_registration,
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)
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from torchgen.executorch.api.types import contextArg, ExecutorchCppSignature
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from torchgen.executorch.api.unboxing import Unboxing
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from torchgen.executorch.model import ETKernelIndex, ETParsedYaml
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from torchgen.gen import (
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get_custom_build_selector,
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get_native_function_declarations,
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get_native_function_declarations_from_ns_grouped_kernels,
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get_native_function_schema_registrations,
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LineLoader,
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parse_native_yaml,
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)
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from torchgen.model import (
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BackendIndex,
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BackendMetadata,
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DEFAULT_KERNEL_NAMESPACE,
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DispatchKey,
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Location,
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NativeFunction,
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NativeFunctionsGroup,
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OperatorName,
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Variant,
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)
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from torchgen.selective_build.selector import SelectiveBuilder
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from torchgen.utils import (
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context,
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FileManager,
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make_file_manager,
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mapMaybe,
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NamespaceHelper,
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)
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def _sig_decl_wrapper(sig: Union[CppSignature, ExecutorchCppSignature]) -> str:
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"""
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A wrapper function to basically get `sig.decl(include_context=True)`.
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For ATen kernel, the codegen has no idea about ET contextArg, so we
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use this wrapper to add it.
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"""
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if isinstance(sig, ExecutorchCppSignature):
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return sig.decl()
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returns_type = aten_cpp.returns_type(sig.func.returns).cpp_type()
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cpp_args = [a.decl() for a in sig.arguments()]
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cpp_args_str = ", ".join([contextArg.decl()] + cpp_args)
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sig_decl = f"{returns_type} {sig.name()}({cpp_args_str})"
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return sig_decl
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def static_dispatch(
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sig: Union[CppSignature, ExecutorchCppSignature],
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f: NativeFunction,
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backend_indices: List[BackendIndex],
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) -> str:
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"""
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For a given `NativeFunction`, find out the corresponding native function and dispatch to it. If zero or more than one
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native function exists, error out. A simplified version of register_dispatch_key.py
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Arguments:
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sig: A CppSignature for this native function we want to use.
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f: NativeFunction to generate static dispatch.
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backend_indices: All available backends.
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Return:
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C++ code to call backend-specific functions, e.g., "return at::native::add(self, other, scale);"
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"""
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if len(backend_indices) == 0 or f.manual_kernel_registration:
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return ""
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backends = [b for b in backend_indices if b.has_kernel(f)]
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static_block = None
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if len(backends) == 1:
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backend_metadata = backends[0].get_kernel(f)
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if backend_metadata:
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args = ", ".join(a.name for a in sig.arguments())
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# Here we are assuming there's no difference between CppSignature and NativeSignature for Executorch.
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static_block = f"return ::{backend_metadata.cpp_namespace}::{backend_metadata.kernel}({args});"
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else:
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static_block = f"""
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ET_ASSERT_UNREACHABLE_MSG("The number of native function(s) binding to {f.func.name} is {len(backends)}.");
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"""
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return f"""
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// {f.namespace}::{f.func}
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TORCH_API inline {_sig_decl_wrapper(sig)} {{
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{static_block}
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}}
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"""
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# Generates Functions.h, which provides the functional public C++ API,
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# and the scaffolding to call into the dispatcher from these functions.
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@dataclass(frozen=True)
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class ComputeFunction:
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static_dispatch_backend_indices: List[BackendIndex]
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selector: SelectiveBuilder
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use_aten_lib: bool
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is_custom_op: Callable[[NativeFunction], bool]
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@method_with_native_function
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def __call__(self, f: NativeFunction) -> Optional[str]:
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if not self.selector.is_root_operator(f"{f.namespace}::{f.func.name}"):
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return None
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if Variant.function not in f.variants:
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return None
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sig: Union[CppSignature, ExecutorchCppSignature] = (
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CppSignatureGroup.from_native_function(
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f, method=False, fallback_binding=f.manual_cpp_binding
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).most_faithful_signature()
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if self.use_aten_lib
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else ExecutorchCppSignature.from_native_function(f)
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)
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if self.use_aten_lib and not self.is_custom_op(f):
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comma = ", "
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return f"""
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// {f.namespace}::{f.func}
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TORCH_API inline {_sig_decl_wrapper(sig)} {{
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return at::{sig.name()}({comma.join(e.name for e in sig.arguments())});
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}}
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"""
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else:
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return static_dispatch(
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sig,
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f,
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backend_indices=self.static_dispatch_backend_indices,
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)
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# Generates RegisterCodegenUnboxedKernels.cpp.
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@dataclass(frozen=True)
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class ComputeCodegenUnboxedKernels:
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selector: SelectiveBuilder
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use_aten_lib: bool
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@method_with_native_function
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def __call__(self, f: NativeFunction) -> str:
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if not self.selector.is_root_operator(f"{f.namespace}::{f.func.name}"):
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return ""
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sig: Union[CppSignature, ExecutorchCppSignature]
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argument_type_gen: Callable[..., NamedCType]
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return_type_gen: Callable[..., CType]
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if self.use_aten_lib:
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sig = CppSignatureGroup.from_native_function(
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f, method=False, fallback_binding=f.manual_cpp_binding
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).most_faithful_signature()
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argument_type_gen = aten_cpp.argumenttype_type
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return_type_gen = aten_cpp.returns_type
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arguments = sig.arguments()
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else:
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sig = ExecutorchCppSignature.from_native_function(f)
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argument_type_gen = et_cpp.argumenttype_type
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return_type_gen = et_cpp.returns_type
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arguments = sig.arguments(include_context=False)
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# parse arguments into C++ code
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binding_list, code_list = Unboxing(
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argument_type_gen=argument_type_gen
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).convert_arguments(arguments)
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# for each C++ argument, generate the conversion code
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code_connector = "\n\t"
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arg_connector = ", "
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args_str = f"{arg_connector.join(e.name for e in binding_list)}"
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if len(f.func.returns) == 0:
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if len(f.func.arguments.out) == 0:
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raise Exception(
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f"Can't handle native function {f.func} with no returns and no out yet."
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)
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out = f.func.arguments.out[0]
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return_assignment = f"""stack[{len(binding_list)}] = &{out.name};"""
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ret_prefix = ""
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else:
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if len(f.func.arguments.out) == 0:
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return_assignment = (
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f"""*stack[{len(binding_list)}] = EValue(result_);"""
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)
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ret_prefix = return_type_gen(f.func.returns).cpp_type() + " result_ = "
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else:
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return_assignment = ""
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ret_prefix = ""
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return f"""
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Operator(
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"{f.namespace}::{f.func.name}",
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[]({contextArg.defn()}, EValue** stack) {{
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{code_connector.join(code_list)}
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EXECUTORCH_SCOPE_PROF("native_call_{f.func.name}");
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{ret_prefix}torch::executor::{f.namespace}::{sig.name()}({"context, "}{args_str});
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{return_assignment}
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}}
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),
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"""
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def gen_unboxing(
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*,
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native_functions: Sequence[NativeFunction],
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cpu_fm: FileManager,
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selector: SelectiveBuilder,
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use_aten_lib: bool,
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) -> None:
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def key_func(fn: Union[NativeFunction, NativeFunctionsGroup]) -> str:
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return fn.root_name
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cpu_fm.write_sharded(
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"RegisterCodegenUnboxedKernels.cpp",
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native_functions,
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key_fn=key_func,
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env_callable=lambda fn: {
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"unboxed_ops": [ComputeCodegenUnboxedKernels(selector, use_aten_lib)(fn)],
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},
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num_shards=1,
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sharded_keys={"unboxed_ops"},
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)
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@with_native_function_and_index # type: ignore[arg-type]
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def compute_native_function_declaration(
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g: Union[NativeFunctionsGroup, NativeFunction], kernel_index: ETKernelIndex
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) -> List[str]:
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assert isinstance(g, NativeFunction)
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sig = ExecutorchCppSignature.from_native_function(f=g)
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metadata_list = kernel_index.get_kernels(g).values()
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if metadata_list is None:
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return []
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prefix = "TORCH_API"
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# for kernels in lean mode, we declare two versions, one with context and one without.
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# In the end we will cleanup the unused one.
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def gen_decl(metadata: BackendMetadata, include_context: bool) -> str:
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return f"{prefix} {sig.decl(name=metadata.kernel, include_context=include_context)};"
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return [
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gen_decl(metadata, include_context)
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for include_context in [False, True]
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for metadata in metadata_list
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]
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def gen_functions_declarations(
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*,
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native_functions: Sequence[NativeFunction],
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kernel_index: ETKernelIndex,
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selector: SelectiveBuilder,
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use_aten_lib: bool,
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custom_ops_native_functions: Optional[Sequence[NativeFunction]] = None,
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) -> str:
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"""
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Generates namespace separated C++ function API inline declaration/definitions.
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Native functions are grouped by namespaces and the generated code is wrapped inside
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namespace blocks.
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E.g., for `custom_1::foo.out` in yaml file we will generate a C++ API as a symbol
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in `torch::executor::custom_1::foo_out`. This way we avoid symbol conflict when
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the other `custom_2::foo.out` is available.
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"""
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# convert kernel index to BackendIndex. This is because we can't handle ETKernelIndex yet.
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# TODO larryliu: evaluate if this code is still needed. If yes let it handle ETKernelIndex.
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dispatch_key = DispatchKey.CPU
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backend_index = kernel_index._to_backend_index()
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ns_grouped_functions = defaultdict(list)
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for native_function in native_functions:
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ns_grouped_functions[native_function.namespace].append(native_function)
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functions_declarations = ""
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newline = "\n"
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for namespace in ns_grouped_functions:
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ns_helper = NamespaceHelper(
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namespace_str=namespace,
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entity_name="",
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max_level=3,
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)
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declarations = list(
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mapMaybe(
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ComputeFunction(
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static_dispatch_backend_indices=[backend_index],
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selector=selector,
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use_aten_lib=use_aten_lib,
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is_custom_op=lambda f: custom_ops_native_functions is not None
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and f in custom_ops_native_functions,
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),
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ns_grouped_functions[namespace],
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)
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)
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functions_declarations += f"""
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{ns_helper.prologue}
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{newline.join(declarations)}
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{ns_helper.epilogue}
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"""
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return functions_declarations
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def get_ns_grouped_kernels(
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*,
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native_functions: Sequence[NativeFunction],
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kernel_index: ETKernelIndex,
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native_function_decl_gen: Callable[
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[
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Union[NativeFunctionsGroup, NativeFunction],
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ETKernelIndex,
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],
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List[str],
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],
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) -> Dict[str, List[str]]:
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ns_grouped_kernels: Dict[str, List[str]] = defaultdict(list)
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for f in native_functions:
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native_function_namespaces = set()
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op_kernels = kernel_index.get_kernels(f)
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for backend_metadata in op_kernels.values():
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if backend_metadata:
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namespace = backend_metadata.cpp_namespace
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native_function_namespaces.add(namespace)
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else:
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namespace = DEFAULT_KERNEL_NAMESPACE
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assert (
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len(native_function_namespaces) <= 1
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), f"Codegen only supports one namespace per operator, got {native_function_namespaces}"
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ns_grouped_kernels[namespace].extend(
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native_function_decl_gen(f, kernel_index)
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)
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return ns_grouped_kernels
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def gen_headers(
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*,
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native_functions: Sequence[NativeFunction],
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gen_custom_ops_header: bool,
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custom_ops_native_functions: Sequence[NativeFunction],
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selector: SelectiveBuilder,
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kernel_index: ETKernelIndex,
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cpu_fm: FileManager,
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use_aten_lib: bool,
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) -> None:
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"""Generate headers.
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Args:
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native_functions (Sequence[NativeFunction]): a collection of NativeFunction for ATen ops.
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gen_custom_ops_header (bool): whether we should generate CustomOpsNativeFunctions.h
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custom_ops_native_functions (Sequence[NativeFunction]): a collection of NativeFunction for custom ops.
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kernel_index (ETKernelIndex): kernel collection
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cpu_fm (FileManager): file manager manages output stream
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use_aten_lib (bool): whether we are generating for PyTorch types or Executorch types.
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"""
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aten_headers = ["#include <ATen/Functions.h>"]
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backend_indices = {DispatchKey.CPU: kernel_index._to_backend_index()}
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if gen_custom_ops_header:
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cpu_fm.write_with_template(
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"CustomOpsNativeFunctions.h",
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"NativeFunctions.h",
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lambda: {
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"nativeFunctions_declarations": get_native_function_declarations(
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grouped_native_functions=custom_ops_native_functions,
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backend_indices=backend_indices,
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native_function_decl_gen=dest.compute_native_function_declaration,
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),
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},
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)
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aten_headers.append('#include "CustomOpsNativeFunctions.h"')
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cpu_fm.write(
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"Functions.h",
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lambda: {
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"static_dispatch_extra_headers": aten_headers
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if use_aten_lib
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else ['#include "NativeFunctions.h"'],
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"Functions_declarations": gen_functions_declarations(
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native_functions=native_functions,
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kernel_index=kernel_index,
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selector=selector,
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use_aten_lib=use_aten_lib,
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custom_ops_native_functions=custom_ops_native_functions,
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),
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},
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)
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if use_aten_lib:
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cpu_fm.write(
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"NativeFunctions.h",
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lambda: {
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"nativeFunctions_declarations": get_native_function_declarations(
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grouped_native_functions=native_functions,
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backend_indices=backend_indices,
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native_function_decl_gen=dest.compute_native_function_declaration,
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),
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},
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)
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else:
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ns_grouped_kernels = get_ns_grouped_kernels(
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native_functions=native_functions,
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kernel_index=kernel_index,
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native_function_decl_gen=compute_native_function_declaration, # type: ignore[arg-type]
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)
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cpu_fm.write(
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"NativeFunctions.h",
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lambda: {
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"nativeFunctions_declarations": get_native_function_declarations_from_ns_grouped_kernels(
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ns_grouped_kernels=ns_grouped_kernels,
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),
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},
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)
|
|
|
|
|
|
def gen_custom_ops(
|
|
*,
|
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native_functions: Sequence[NativeFunction],
|
|
selector: SelectiveBuilder,
|
|
kernel_index: ETKernelIndex,
|
|
cpu_fm: FileManager,
|
|
rocm: bool,
|
|
) -> None:
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|
dispatch_key = DispatchKey.CPU
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|
(
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anonymous_definition,
|
|
static_init_dispatch_registrations,
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|
) = gen_custom_ops_registration(
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native_functions=native_functions,
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selector=selector,
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kernel_index=kernel_index,
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rocm=rocm,
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)
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|
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,
|
|
},
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|
)
|
|
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(
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mapMaybe(ComputeNativeFunctionStub(), native_functions)
|
|
),
|
|
"static_init_dispatch_registrations": static_init_dispatch_registrations,
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|
},
|
|
)
|
|
|
|
(
|
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aten_schema_registrations,
|
|
schema_registrations,
|
|
) = get_native_function_schema_registrations(
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native_functions=native_functions,
|
|
schema_selector=selector,
|
|
)
|
|
cpu_fm.write(
|
|
"RegisterSchema.cpp",
|
|
lambda: {
|
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"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: Optional[str],
|
|
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, "r") as aten_yaml:
|
|
out_file.writelines(aten_yaml.readlines())
|
|
return
|
|
aten_parsed_yaml = parse_native_yaml(
|
|
aten_yaml_path,
|
|
tags_yaml_path,
|
|
None,
|
|
skip_native_fns_gen=False,
|
|
)
|
|
aten_native_functions = aten_parsed_yaml.native_functions
|
|
schema_dict = {
|
|
f"{f.namespace}::{f.func.name}": str(f.func) for f in aten_native_functions
|
|
}
|
|
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, "r") 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)
|
|
yaml.dump(native_es, out_file, width=1000)
|
|
|
|
|
|
def parse_yaml(
|
|
path: Optional[str],
|
|
tags_yaml_path: str,
|
|
function_filter: Callable[[NativeFunction], bool],
|
|
skip_native_fns_gen: bool = False,
|
|
) -> Tuple[
|
|
List[NativeFunction], Dict[DispatchKey, Dict[OperatorName, BackendMetadata]]
|
|
]:
|
|
if path and os.path.exists(path) and os.stat(path).st_size > 0:
|
|
parsed_yaml = parse_native_yaml(
|
|
path,
|
|
tags_yaml_path,
|
|
None,
|
|
skip_native_fns_gen=skip_native_fns_gen,
|
|
)
|
|
native_functions = list(filter(function_filter, parsed_yaml.native_functions))
|
|
op_names = [f.func.name for f in native_functions]
|
|
|
|
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: Optional[str],
|
|
custom_ops_yaml_path: Optional[str],
|
|
selector: SelectiveBuilder,
|
|
use_aten_lib: bool,
|
|
) -> Tuple[ETParsedYaml, Optional[ETParsedYaml]]:
|
|
"""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_backend_indices = parse_yaml(
|
|
translated_yaml_path, tags_yaml_path, function_filter, not use_aten_lib
|
|
)
|
|
custom_ops_functions, custom_ops_backend_indices = parse_yaml(
|
|
custom_ops_yaml_path, tags_yaml_path, function_filter, True
|
|
)
|
|
|
|
combined_functions = translated_functions + custom_ops_functions
|
|
combined_kernel_index: ETKernelIndex = ETKernelIndex.from_backend_indices(
|
|
translated_backend_indices
|
|
).grow(custom_ops_backend_indices)
|
|
custom_ops_kernel_index: ETKernelIndex = ETKernelIndex.from_backend_indices(
|
|
custom_ops_backend_indices
|
|
)
|
|
combined_yaml = ETParsedYaml(combined_functions, combined_kernel_index)
|
|
custom_ops_parsed_yaml = ETParsedYaml(
|
|
custom_ops_functions, custom_ops_kernel_index
|
|
)
|
|
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(
|
|
"--generate",
|
|
type=str,
|
|
nargs="*",
|
|
choices=["headers", "sources"],
|
|
default=["headers", "sources"],
|
|
help="Generate only a subset of files",
|
|
)
|
|
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,
|
|
)
|
|
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 = pathlib.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()
|