mirror of
https://github.com/pytorch/pytorch.git
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a8e45b5969
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/75274 - default to generating forced fallback for TS backend (where it is used for tests/debugging, but false otherwise Test Plan: Imported from OSS Reviewed By: bdhirsh Differential Revision: D35411211 Pulled By: wconstab fbshipit-source-id: ccff2f65aa5d8e1aa670d210ce51805985df55ce (cherry picked from commit 55b48cc02497686f4e25ed3c6dcf9b6b77d49136)
362 lines
18 KiB
Python
362 lines
18 KiB
Python
import pathlib
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import argparse
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import os
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import re
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import yaml
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from collections import namedtuple, Counter
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from typing import List, Dict, Union, Sequence, Optional, Callable, Iterable, Iterator, Tuple, Type
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from tools.codegen.dest.lazy_ir import LazyIR, TSLazyIR
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from tools.codegen.gen import get_grouped_native_functions, parse_native_yaml, NamespaceHelper
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from tools.codegen.model import (FunctionSchema,
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NativeFunction, NativeFunctionsGroup, OperatorName)
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from tools.codegen.selective_build.selector import SelectiveBuilder
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from tools.codegen.utils import concatMap, YamlLoader, FileManager
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import tools.codegen.dest as dest
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from .gen_backend_stubs import (parse_backend_yaml, error_on_missing_kernels,
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gen_dispatchkey_nativefunc_headers,
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gen_dispatcher_registrations)
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
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#
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# Lazy Tensor Codegen
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#
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
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# Overview
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# ~~~~~~~~
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#
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# This codegen script builds on existing data models and helpers used
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# by all ATen backends, and adds new functionality specific to lazy
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# tensor backends.
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#
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# Inputs:
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# - <backend>_native_functions.yaml: controls which operators are
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# supported by the backend.
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#
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# Outputs:
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# (for all backends)
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# <DispatchKey>Ir.h defines Lazy IR classes to be constructed during tracing
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# - opt-in: also generate 'lowering' methods for the TorchScript backend only
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# <DispatchKey>NativeFunctions.cpp defines implementations of native functions which perform lazy tracing
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# - opt-in: 'full_codegen' section of backend yaml; 'supported' section omits these implementations
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# <DispatchKey>NativeFunctions.h declares implementations of native functions for both 'supported' and 'full_codegen'
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# ops
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#
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# Register<DispatchKey>.cpp registers all op implementations with the dispatcher
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# RegisterAutograd<DispatchKey>.cpp registers all autograd implementations with the dispatcher
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#
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# Validation Helpers:
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# - Shape Inference: errs if any ops in backend yaml require shape inference not provided by meta kernels or
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# implementations in torch/csrc/lazy/core/shape_inference.*
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# - native function impls: errs if any 'supported' ops do not have an implementation defined in the backend
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# (non-codegen) implementation file
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#
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#
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# About the Data Model
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# ~~~~~~~~~~~~~~~~~~~~
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#
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# Modeled after ATen codegen, the first step is to parse yaml and build a data model for the operators
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# we care about. In this case, the <backend>_native_functions yaml defines a subset of the core operators
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# (defined in more detail in the main native_functions.yaml), which will be supported by your backend.
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# Backends can list ops in two categories:
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# - `supported` ops require hand-implementations but still get codegenned declarations and registrations
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# - `full_codegen` ops get implementations (and IR classes) generated too
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#
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# Each native function is modeled as an object with a schema, and each schema has objects representing their
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# arguments. Much of the codegen is manipulation of the arguments and their types. For example, lazy tensor
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# backends need to transform 'at::Tensor' arguments into 'lazy::Value' objects, as well as replacing reference
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# types (stringref) with actual string objects, and this is done by manipulating the data model objects.
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# - see api/lazy.py for the lazy data model
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#
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# Once the data model is set up, the rest of this script processes a number of templates for output CPP file
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# and fills in the template values using helpers in `dest/lazy_ir.py` and `dest/lazy_ts_lowering.py`. These
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# helpers mostly iterate over functions and their arguments, outputting different c++ snippets.
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#
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
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# Parses the external backend's yaml, and adds a new BackendIndex for the backend's dispatch key.
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# Returns a Tuple of (backend_key, autograd_key, cpp_namespace, updated BackendIndex mapping, full_codegen)
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ParsedExternalYaml = namedtuple('ParsedExternalYaml', [
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'backend_key', 'autograd_key', 'cpp_namespace', 'backend_indices', 'full_codegen'])
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def parse_full_codegen_ops(
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backend_yaml_path: str,
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grouped_native_functions: Sequence[Union[NativeFunction, NativeFunctionsGroup]],
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) -> List[OperatorName]:
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native_functions_map: Dict[OperatorName, NativeFunction] = {
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f.func.name: f
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for f in concatMap(
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lambda f: [f] if isinstance(f, NativeFunction) else list(f.functions()), grouped_native_functions
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)
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}
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with open(backend_yaml_path, 'r') as f:
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yaml_values = yaml.load(f, Loader=YamlLoader)
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assert isinstance(yaml_values, dict)
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full_codegen = yaml_values.pop('full_codegen', [])
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assert isinstance(full_codegen, list), f'expected "full_codegen" to be a list, but got: {full_codegen}'
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full_codegen = [OperatorName.parse(name) for name in full_codegen]
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return full_codegen
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def validate_shape_inference_header(shape_inference_hdr: str, expected_shape_infr_decls: List[str]) -> None:
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try:
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with open(shape_inference_hdr, 'r') as f:
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shape_infr_decls = f.read()
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shape_infr_decl_lines = set(shape_infr_decls.split("\n"))
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except IOError:
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raise AssertionError(f'Unable to read from the specified shape_inference_hdr file: {shape_inference_hdr}')
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shape_infr_regex = r'compute_shape_(\w+)'
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actual_shape_infr_name_counts = Counter(re.findall(shape_infr_regex, shape_infr_decls))
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# TODO(whc) add a check for shape inference functions that have meta kernels implement and should be retired.
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for decl in expected_shape_infr_decls:
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assert decl in shape_infr_decl_lines, f"""Missing shape inference function.\n
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Please add declare this function in {shape_inference_hdr}:\n
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and implement it in the the corresponding shape_inference.cpp file.\n
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{decl}"""
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class default_args:
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node_base: str = "Node"
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node_base_hdr: Optional[str] = None
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shape_inference_hdr: str = "torch/csrc/lazy/core/shape_inference.h"
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tensor_class: str = "torch::lazy::LazyTensor"
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tensor_class_hdr: str = "torch/csrc/lazy/core/tensor.h"
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lazy_ir_cls: Type[LazyIR] = LazyIR
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backend_name: str = "TorchScript"
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def main() -> None:
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parser = argparse.ArgumentParser(description='Generate Lazy Tensor backend files')
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parser.add_argument(
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'-s',
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'--source_yaml',
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help='path to source yaml file containing operator external definitions')
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parser.add_argument(
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'-o', '--output_dir', help='output directory')
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parser.add_argument(
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'--dry_run', type=bool, default=False, help='output directory')
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parser.add_argument(
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'--impl_path', type=str, default=None, help='path to the source C++ file containing kernel definitions')
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parser.add_argument(
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'--gen_ts_lowerings', action="store_true",
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help='Generate TorchScript lowerings in addition to Lazy IR and NativeFunctions')
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parser.add_argument(
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'--node_base', type=str, default=default_args.node_base,
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help='Name of backend specific custom Lazy IR Node base class')
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parser.add_argument(
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'--node_base_hdr', type=str, default=default_args.node_base_hdr,
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help='Path to header file defining custom Lazy IR Node base class')
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parser.add_argument(
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'--shape_inference_hdr', type=str, default=default_args.shape_inference_hdr,
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help='Path to header file defining custom Lazy shape inference functions')
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parser.add_argument(
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'--tensor_class', type=str, default=default_args.tensor_class,
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help='Name of backend specific custom Lazy Tensor class')
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parser.add_argument(
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'--tensor_class_hdr', type=str, default=default_args.tensor_class_hdr,
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help='Path to header file defining custom Lazy Tensor class')
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parser.add_argument(
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'--backend_name', type=str, default=default_args.backend_name,
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help='Name of the backend to generate')
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options = parser.parse_args()
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# Assumes that this file lives at PYTORCH_ROOT/tools/codegen/gen_backend_stubs.py
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torch_root = pathlib.Path(__file__).parent.parent.parent.absolute()
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aten_path = str(torch_root / "aten" / "src" / "ATen")
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ir_gen_class: Type[LazyIR] = default_args.lazy_ir_cls
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if options.gen_ts_lowerings:
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ir_gen_class = TSLazyIR
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run_gen_lazy_tensor(aten_path, options.source_yaml, options.output_dir, options.dry_run, options.impl_path,
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options.node_base, options.node_base_hdr,
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options.tensor_class, options.tensor_class_hdr, options.shape_inference_hdr,
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ir_gen_class, options.backend_name)
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def run_gen_lazy_tensor(aten_path: str, source_yaml: str, output_dir: str,
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dry_run: bool, impl_path: Optional[str],
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node_base: str = default_args.node_base,
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node_base_hdr: Optional[str] = default_args.node_base_hdr,
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tensor_class: str = default_args.tensor_class,
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tensor_class_hdr: str = default_args.tensor_class_hdr,
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shape_inference_hdr: str = default_args.shape_inference_hdr,
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lazy_ir_cls: Type[LazyIR] = default_args.lazy_ir_cls,
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# build_in_tree is true for TS backend and affects include paths
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build_in_tree: bool = False,
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# per_operator_headers changes whether ATen/Functions.h or individual operator headers are used
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# it must match how ATen was built
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per_operator_headers: bool = False,
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backend_name: str = default_args.backend_name,
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gen_forced_fallback_code: bool = False) -> None:
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template_dir = os.path.join(aten_path, "templates")
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def make_file_manager(install_dir: str) -> FileManager:
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return FileManager(install_dir=install_dir, template_dir=template_dir, dry_run=dry_run)
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fm = make_file_manager(output_dir)
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native_yaml_path = os.path.join(aten_path, 'native/native_functions.yaml')
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parsed_yaml = parse_native_yaml(native_yaml_path)
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native_functions, backend_indices = parsed_yaml.native_functions, parsed_yaml.backend_indices
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grouped_native_functions = get_grouped_native_functions(native_functions)
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def sort_native_function(f: Union[NativeFunctionsGroup, NativeFunction]) -> str:
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"""
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We sort the native function because of the note in concat_map_codegen.
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TODO(alanwaketan): Remove this sorting hack once all ops are grouped properly.
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"""
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func = f.functional.func if isinstance(f, NativeFunctionsGroup) else f.func
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return str(func.name.name)
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grouped_native_functions = sorted(grouped_native_functions, key=sort_native_function)
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parsed_backend_yaml = parse_backend_yaml(source_yaml, grouped_native_functions, backend_indices)
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backend_key = parsed_backend_yaml.backend_key
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autograd_key = parsed_backend_yaml.autograd_key
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cpp_namespace = parsed_backend_yaml.cpp_namespace
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backend_indices = parsed_backend_yaml.backend_indices
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full_codegen = parse_full_codegen_ops(source_yaml, grouped_native_functions)
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def concat_map_codegen(func: Callable[[NativeFunction], Sequence[str]],
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xs: Iterable[Union[NativeFunctionsGroup, NativeFunction]],
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*, codegenInplaceVariant: bool = False) -> Iterator[str]:
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"""
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We code-gen for the functional variant, which is all we need for IR classes/lowerings/shape inferences, but we
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only code-gen additional entries for the inplace variant for the native functions.
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Note: If xs is not sorted, there may be an edge case when generating IR classes. Considering relu and relu_, if
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we encounter relu_ before relu. we will then generate an IR class with op = at::aten::relu_ for both relu and
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relu_ which will cause problems for relu.
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TODO(alanwaketan): Once all ops are grouped properly, we should no longer need this hack.
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"""
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generated = set()
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def gen_key(func: FunctionSchema) -> Tuple[str, str]:
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# we want to generate unique entries for overloads of functional variants,
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# but not for inplace variants unless explicitly told `codegenInplaceVariant`
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return (func.name.name.base, func.name.overload_name)
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for x in xs:
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f = x.functional if isinstance(x, NativeFunctionsGroup) else x
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# For the 'or'd terms:
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# 1. codegenInplaceVariant means we can generate the in-place variant corresponding items.
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# 2. not f.func.name.name.inplace means the op is not a in-place variant, so we can generate the item.
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# 3. f.func.name.name.base not in generated means even for in-place ops we still need to generate the item
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# as if they were the functional variants for one time.
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if f.func.name in full_codegen and \
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(codegenInplaceVariant or not f.func.name.name.inplace or gen_key(f.func) not in generated):
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generated.add(gen_key(f.func))
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for r in func(f):
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yield r
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selector = SelectiveBuilder.get_nop_selector()
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assert backend_key is not None
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class_name = backend_indices[backend_key].native_function_class_name()
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if impl_path is not None:
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error_on_missing_kernels(native_functions, backend_indices, backend_key,
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autograd_key, class_name, impl_path, full_codegen)
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""" Validate Shape Inference Definitions
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Generated lazy native functions all perform shape inference, by first using a meta:: kernel
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if available for that op, and otherwise using a 'compute_shape_{op}' function instead. The generator
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knows the call signature for compute_shape_{op} becuase it matches the nativefunction (and meta::) signature,
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so it just has to check whether the op is structured and generate a call for one or the other. It's up to the dev
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to supply the missing compute_shape_{op} function, but the codegen at least warns you about this and provides
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the expected signature which can be copy-pasted into shape_inference.h.
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compute_shape_{op} functions are handwritten and should be replaced over time as ops get ported
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to structured kernels.
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See torch/csrc/lazy/core/shape_inference.cpp #READ THIS! for more information.
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"""
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if shape_inference_hdr is not None:
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expected_shape_infr_decls = list(
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concat_map_codegen(
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dest.GenLazyShapeInferenceDefinition(backend_indices[backend_key], tensor_class),
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grouped_native_functions,
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codegenInplaceVariant=True
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)
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)
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validate_shape_inference_header(shape_inference_hdr, expected_shape_infr_decls)
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assert class_name is not None
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# Generate nativefunction declarations
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# Note, eager registrations is set to False for the lazy TS backend as another LTC backend
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# may want to register their own lazy kernels instead of registering the TS ones.
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# The registration will lazily happen when init_ts_backend is called.
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gen_dispatchkey_nativefunc_headers(fm, class_name, cpp_namespace, backend_indices,
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grouped_native_functions, backend_key, autograd_key,
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backend_name)
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# Generate Dispatcher registrations which hook up the nativefunctions
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for dispatch_key in [backend_key] if autograd_key is None else [backend_key, autograd_key]:
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gen_dispatcher_registrations(fm, output_dir, class_name, cpp_namespace, backend_indices, grouped_native_functions,
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backend_key, dispatch_key, selector,
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build_in_tree=build_in_tree,
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per_operator_headers=per_operator_headers,
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backend_name=backend_name,
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eager_registration=False)
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# Generate native function impls that build IR nodes
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ns_helper = NamespaceHelper(cpp_namespace)
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fm.write_with_template(f'{backend_key}NativeFunctions.cpp', 'DispatchKeyNativeFunctions.cpp', lambda: {
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'includes': [f'#include <{path}>' for path in [
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tensor_class_hdr,
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shape_inference_hdr,
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"ATen/Functions.h",
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"ATen/MetaFunctions.h",
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"ATen/Operators.h",
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"ATen/native/CPUFallback.h",
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"torch/csrc/lazy/core/lazy_graph_executor.h",
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"torch/csrc/lazy/core/metrics.h",
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"torch/csrc/lazy/core/shape.h",
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f"{output_dir}/{backend_key}NativeFunctions.h",
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f"{output_dir}/LazyIr.h",
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] + (["torch/csrc/lazy/ts_backend/ts_eager_fallback.h"] if gen_forced_fallback_code else [])],
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'native_functions_include': '',
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'namespace_prologue': ns_helper.prologue,
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'namespace_epilogue': ns_helper.epilogue,
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'native_function_definitions':
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list(concat_map_codegen(
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dest.GenLazyNativeFuncDefinition(f'{backend_key}NativeFunctions',
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backend_indices[backend_key],
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tensor_class,
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gen_forced_fallback_code),
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grouped_native_functions,
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codegenInplaceVariant=True
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)),
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})
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# Generate IR node classes
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fm.write_with_template('LazyIr.h', 'LazyIr.h', lambda: {
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'lazy_ir_sysinc': [f'#include <{path}>' for path in [
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"ATen/core/Formatting.h",
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"c10/core/ScalarType.h",
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"c10/util/Optional.h",
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"torch/csrc/lazy/core/hash.h",
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"torch/csrc/lazy/core/ir.h",
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"torch/csrc/lazy/core/shape.h",
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"vector",
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]],
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'lazy_ir_inc': [f'#include "{path}"' for path in [
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node_base_hdr if node_base_hdr is not None else None
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] if path is not None],
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'ir_declarations': list(concat_map_codegen(
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lazy_ir_cls(backend_indices[backend_key], node_base),
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grouped_native_functions
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)),
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'namespace_prologue': ns_helper.prologue,
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'namespace_epilogue': ns_helper.epilogue,
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})
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if __name__ == '__main__':
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main()
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