torchgen/gen_lazy_tensor.py

from __future__ import annotations

import argparse
import os
from collections import namedtuple
from pathlib import Path
from typing import Any, Callable, Iterable, Iterator, Sequence

import yaml

import torchgen.dest as dest
from torchgen.api.lazy import setValueT
from torchgen.api.types import BaseCppType
from torchgen.dest.lazy_ir import GenLazyIR, GenLazyNativeFuncDefinition, GenTSLazyIR
from torchgen.gen import get_grouped_native_functions, parse_native_yaml
from torchgen.gen_backend_stubs import (
    error_on_missing_kernels,
    gen_dispatcher_registrations,
    gen_dispatchkey_nativefunc_headers,
    parse_backend_yaml,
)
from torchgen.model import NativeFunction, NativeFunctionsGroup, OperatorName
from torchgen.selective_build.selector import SelectiveBuilder
from torchgen.utils import FileManager, NamespaceHelper
from torchgen.yaml_utils import YamlLoader


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
#                        Lazy Tensor Codegen
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
# Overview
# ~~~~~~~~
#
# This codegen script builds on existing data models and helpers used
# by all ATen backends, and adds new functionality specific to lazy
# tensor backends.
#
# Inputs:
# - <backend>_native_functions.yaml: controls which operators are
#   supported by the backend.
#
# Outputs:
# (for all backends)
# <DispatchKey>Ir.h defines Lazy IR classes to be constructed during tracing
# - opt-in: also generate 'lowering' methods for the TorchScript backend only
# <DispatchKey>NativeFunctions.cpp defines implementations of native functions which perform lazy tracing
# - opt-in: 'full_codegen' section of backend yaml; 'supported' section omits these implementations
# <DispatchKey>NativeFunctions.h declares implementations of native functions for both 'supported' and 'full_codegen'
# ops
#
# Register<DispatchKey>.cpp registers all op implementations with the dispatcher
# RegisterAutograd<DispatchKey>.cpp registers all autograd implementations with the dispatcher
#
# Validation Helpers:
# - Shape Inference: errs if any ops in backend yaml require shape inference not provided by meta kernels or
#   implementations in torch/csrc/lazy/core/shape_inference.*
# - native function impls: errs if any 'supported' ops do not have an implementation defined in the backend
#   (non-codegen) implementation file
#
#
# About the Data Model
# ~~~~~~~~~~~~~~~~~~~~
#
# Modeled after ATen codegen, the first step is to parse yaml and build a data model for the operators
# we care about.  In this case, the <backend>_native_functions yaml defines a subset of the core operators
# (defined in more detail in the main native_functions.yaml), which will be supported by your backend.
# Backends can list ops in two categories:
#  - `supported` ops require hand-implementations but still get codegenned declarations and registrations
#  - `full_codegen` ops get implementations (and IR classes) generated too
#
# Each native function is modeled as an object with a schema, and each schema has objects representing their
# arguments.  Much of the codegen is manipulation of the arguments and their types.  For example, lazy tensor
# backends need to transform 'at::Tensor' arguments into 'lazy::Value' objects, as well as replacing reference
# types (stringref) with actual string objects, and this is done by manipulating the data model objects.
# - see api/lazy.py for the lazy data model
#
# Once the data model is set up, the rest of this script processes a number of templates for output CPP file
# and fills in the template values using helpers in `dest/lazy_ir.py` and `dest/lazy_ts_lowering.py`.  These
# helpers mostly iterate over functions and their arguments, outputting different c++ snippets.
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #


# Parses the external backend's yaml, and adds a new BackendIndex for the backend's dispatch key.
# Returns a Tuple of (backend_key, autograd_key, cpp_namespace, updated BackendIndex mapping, full_codegen)
ParsedExternalYaml = namedtuple(
    "ParsedExternalYaml",
    ["backend_key", "autograd_key", "cpp_namespace", "backend_indices", "full_codegen"],
)


def parse_native_functions_keys(
    backend_yaml_path: str,
    grouped_native_functions: Sequence[NativeFunction | NativeFunctionsGroup],
) -> tuple[list[OperatorName], list[Any], list[OperatorName]]:
    with open(backend_yaml_path) as f:
        yaml_values = yaml.load(f, Loader=YamlLoader)
    assert isinstance(yaml_values, dict)

    full_codegen = yaml_values.pop("full_codegen", [])
    non_native = yaml_values.pop("non_native", [])
    ir_gen = yaml_values.pop("ir_gen", [])
    assert isinstance(full_codegen, list)
    assert isinstance(non_native, list)
    assert isinstance(ir_gen, list)
    full_codegen_opnames = [OperatorName.parse(name) for name in full_codegen]
    ir_gen_opnames = [OperatorName.parse(name) for name in ir_gen]
    return full_codegen_opnames, non_native, ir_gen_opnames


def validate_shape_inference_header(
    shape_inference_hdr: str, expected_shape_infr_decls: list[str]
) -> None:
    try:
        with open(shape_inference_hdr) as f:
            shape_infr_decls = f.read()
            shape_infr_decl_lines = set(shape_infr_decls.split("\n"))
    except OSError as e:
        raise AssertionError(
            f"Unable to read from the specified shape_inference_hdr file: {shape_inference_hdr}"
        ) from e

    # TODO(whc) add a check for shape inference functions that have meta kernels implement and should be retired.

    missing_decls = [
        decl for decl in expected_shape_infr_decls if decl not in shape_infr_decl_lines
    ]
    if missing_decls:
        raise Exception(  # noqa: TRY002
            f"""Missing shape inference function.\n
Please add declare this function in {shape_inference_hdr}:\n
and implement it in the corresponding shape_inference.cpp file.\n
{os.linesep.join(missing_decls)}"""
        )


# Some helper functions for the codegen.
def get_ltc_helper_fns() -> str:
    return """\
at::Tensor to_meta(const at::Tensor& tensor) {
  // undefined tensors can't be converted to the meta device, since they don't have sizes/strides
  if (!tensor.defined()) return tensor;
  auto out = at::native::empty_strided_meta_symint(tensor.sym_sizes(), tensor.sym_strides(), \
/*dtype=*/std::make_optional(tensor.scalar_type()), /*layout=*/std::make_optional(tensor.layout()), \
/*device=*/std::make_optional(c10::Device(c10::kMeta)), /*pin_memory=*/std::nullopt);
  // needs to handle wrapped numbers, so dtype promotion works properly.
  if (tensor.unsafeGetTensorImpl()->is_wrapped_number()) {
    out.unsafeGetTensorImpl()->set_wrapped_number(true);
  }
  return out;
}
std::optional<at::Tensor> to_meta(const std::optional<at::Tensor>& tensor) {
  if (tensor.has_value()) {
    return to_meta(*tensor);
  }
  return std::nullopt;
}

std::vector<at::Tensor> to_meta(at::ITensorListRef t_list) {
  std::vector<at::Tensor> outs;
  outs.reserve(t_list.size());
  for (const auto& tensor : t_list) {
    outs.push_back(to_meta(tensor));
  }
  return outs;
}
"""


class default_args:
    node_base: str = "Node"
    node_base_hdr: str | None = None
    shape_inference_hdr: str = "torch/csrc/lazy/core/shape_inference.h"
    tensor_class: str = "torch::lazy::LazyTensor"
    tensor_class_hdr: str = "torch/csrc/lazy/core/tensor.h"
    lazy_ir_generator: type[GenLazyIR] = GenLazyIR
    native_func_definition_generator: type[
        GenLazyNativeFuncDefinition
    ] = GenLazyNativeFuncDefinition
    backend_name: str = "TorchScript"


def main() -> None:
    parser = argparse.ArgumentParser(description="Generate Lazy Tensor backend files")
    parser.add_argument(
        "-s",
        "--source-yaml",
        "--source_yaml",
        help="path to source yaml file containing operator external definitions",
    )
    parser.add_argument("-o", "--output-dir", "--output_dir", help="output directory")
    parser.add_argument(
        "--dry-run", "--dry_run", type=bool, default=False, help="output directory"
    )
    parser.add_argument(
        "--impl-path",
        "--impl_path",
        type=str,
        default=None,
        help="path to the source C++ file containing kernel definitions",
    )
    parser.add_argument(
        "--gen-ts-lowerings",
        "--gen_ts_lowerings",
        action="store_true",
        help="Generate TorchScript lowerings in addition to Lazy IR and NativeFunctions",
    )
    parser.add_argument(
        "--node-base",
        "--node_base",
        type=str,
        default=default_args.node_base,
        help="Name of backend specific custom Lazy IR Node base class",
    )
    parser.add_argument(
        "--node-base-hdr",
        "--node_base_hdr",
        type=str,
        default=default_args.node_base_hdr,
        help="Path to header file defining custom Lazy IR Node base class",
    )
    parser.add_argument(
        "--shape-inference-hdr",
        "--shape_inference_hdr",
        type=str,
        default=default_args.shape_inference_hdr,
        help="Path to header file defining custom Lazy shape inference functions",
    )
    parser.add_argument(
        "--tensor-class",
        "--tensor_class",
        type=str,
        default=default_args.tensor_class,
        help="Name of backend specific custom Lazy Tensor class",
    )
    parser.add_argument(
        "--tensor-class-hdr",
        "--tensor_class_hdr",
        type=str,
        default=default_args.tensor_class_hdr,
        help="Path to header file defining custom Lazy Tensor class",
    )
    parser.add_argument(
        "--backend-name",
        "--backend_name",
        type=str,
        default=default_args.backend_name,
        help="Name of the backend to generate",
    )
    options = parser.parse_args()

    # Assumes that this file lives at PYTORCH_ROOT/torchgen/gen_backend_stubs.py
    torch_root = Path(__file__).parent.parent.parent.absolute()
    aten_path = str(torch_root / "aten" / "src" / "ATen")
    lazy_ir_generator: type[GenLazyIR] = default_args.lazy_ir_generator
    if options.gen_ts_lowerings:
        lazy_ir_generator = GenTSLazyIR
    native_func_definition_generator: type[
        GenLazyNativeFuncDefinition
    ] = default_args.native_func_definition_generator

    run_gen_lazy_tensor(
        aten_path,
        options.source_yaml,
        options.output_dir,
        options.dry_run,
        options.impl_path,
        options.node_base,
        options.node_base_hdr,
        options.tensor_class,
        options.tensor_class_hdr,
        options.shape_inference_hdr,
        lazy_ir_generator,
        native_func_definition_generator,
        options.backend_name,
    )


def run_gen_lazy_tensor(
    aten_path: str,
    source_yaml: str,
    output_dir: str,
    dry_run: bool,
    impl_path: str | None,
    node_base: str = default_args.node_base,
    node_base_hdr: str | None = default_args.node_base_hdr,
    tensor_class: str = default_args.tensor_class,
    tensor_class_hdr: str = default_args.tensor_class_hdr,
    shape_inference_hdr: str = default_args.shape_inference_hdr,
    lazy_ir_generator: type[GenLazyIR] = default_args.lazy_ir_generator,
    native_func_definition_generator: type[
        GenLazyNativeFuncDefinition
    ] = default_args.native_func_definition_generator,
    # build_in_tree is true for TS backend and affects include paths
    build_in_tree: bool = False,
    # per_operator_headers changes whether ATen/Functions.h or individual operator headers are used
    # it must match how ATen was built
    per_operator_headers: bool = False,
    backend_name: str = default_args.backend_name,
    gen_forced_fallback_code: bool = False,
    use_lazy_shape: bool = True,
    # the following arguments are temporary customization points for xla backend migration.
    # do not rely on them otherwise, they should be removed once migration is complete
    backend_namespace: str = "torch::lazy",
    get_tensorlist: str = "GetTensorList",
    get_tensor_or_wrap_number: str = "GetLtcTensorOrCreateForWrappedNumber",
    try_get_tensor: str = "TryGetLtcTensor",
    metrics_counter: str = 'TORCH_LAZY_FN_COUNTER("lazy::")',
    create_tensor: str = "LazyTensor::Create",
    create_from_first_tensor: bool = False,
    create_aten_from_ltc_tensor: str = "torch::lazy::CreateAtenFromLtcTensor",
    tuple_aten_from_ltc_tensors: str = "torch::lazy::TupleAtenFromLtcTensors",
    lazy_value_class: str = "torch::lazy::Value",
    lazy_tensor_ptr: str = "LazyTensorPtr",
    get_device_fn: str = "torch::lazy::GetBackendDevice",
) -> None:
    lv_tokens = lazy_value_class.split("::")
    lv_class = lv_tokens[-1]
    lv_ns = "::".join(lv_tokens[:-1])
    setValueT(BaseCppType(lv_ns, lv_class))
    template_dir = os.path.join(aten_path, "templates")

    def make_file_manager(install_dir: str) -> FileManager:
        return FileManager(
            install_dir=install_dir, template_dir=template_dir, dry_run=dry_run
        )

    fm = make_file_manager(output_dir)

    native_yaml_path = os.path.join(aten_path, "native/native_functions.yaml")
    tags_yaml_path = os.path.join(aten_path, "native/tags.yaml")
    parsed_yaml = parse_native_yaml(native_yaml_path, tags_yaml_path)
    native_functions, backend_indices = (
        parsed_yaml.native_functions,
        parsed_yaml.backend_indices,
    )
    grouped_native_functions = get_grouped_native_functions(native_functions)

    def sort_native_function(f: NativeFunctionsGroup | NativeFunction) -> str:
        """
        We sort the native function because of the note in concat_map_codegen.
        TODO(alanwaketan): Remove this sorting hack once all ops are grouped properly.
        """
        func = f.functional.func if isinstance(f, NativeFunctionsGroup) else f.func
        return str(func.name.name)

    grouped_native_functions = sorted(
        grouped_native_functions, key=sort_native_function
    )

    parsed_backend_yaml = parse_backend_yaml(
        source_yaml, grouped_native_functions, backend_indices
    )
    backend_key = parsed_backend_yaml.backend_key
    autograd_key = parsed_backend_yaml.autograd_key
    cpp_namespace = parsed_backend_yaml.cpp_namespace
    backend_indices = parsed_backend_yaml.backend_indices
    # the following 3 keys are all processed differently
    # for full_codegen, we generate IR, kernels, etc
    # for ir_gen, we generate only IR
    # non_native is used to register kernels not declared in
    # native_functions.yaml
    full_codegen, non_native, ir_gen = parse_native_functions_keys(
        source_yaml, grouped_native_functions
    )

    def concat_map_codegen(
        func: Callable[[NativeFunction], Sequence[str]],
        xs: Iterable[NativeFunctionsGroup | NativeFunction],
        ops_list: list[OperatorName] = full_codegen,
    ) -> Iterator[str]:
        """
        We code-gen for the functional variant, which is all we need for IR classes/lowerings/shape inferences, but we
        only code-gen additional entries for the inplace variant for the native functions.
        """

        for x in xs:
            fs = list(x.functions()) if isinstance(x, NativeFunctionsGroup) else [x]
            for f in fs:
                if f.func.name in ops_list:
                    yield from func(f)

    selector = SelectiveBuilder.get_nop_selector()

    assert backend_key is not None
    class_name = backend_indices[backend_key].native_function_class_name()

    if impl_path is not None:
        error_on_missing_kernels(
            native_functions,
            backend_indices,
            backend_key,
            autograd_key,
            class_name,
            impl_path,
            full_codegen,
        )

    """ Validate Shape Inference Definitions

    Generated lazy native functions all perform shape inference, by first using a meta:: kernel
    if available for that op, and otherwise using a 'compute_shape_{op}' function instead.  The generator
    knows the call signature for compute_shape_{op} because it matches the nativefunction (and meta::) signature,
    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
    to supply the missing compute_shape_{op} function, but the codegen at least warns you about this and provides
    the expected signature which can be copy-pasted into shape_inference.h.

    compute_shape_{op} functions are handwritten and should be replaced over time as ops get ported
    to structured kernels.

    See torch/csrc/lazy/core/shape_inference.cpp #READ THIS! for more information.
    """
    if shape_inference_hdr is not None:
        expected_shape_infr_decls = list(
            concat_map_codegen(
                dest.GenLazyShapeInferenceDefinition(
                    backend_indices[backend_key], tensor_class
                ),
                grouped_native_functions,
            )
        )

        validate_shape_inference_header(shape_inference_hdr, expected_shape_infr_decls)
    assert class_name is not None

    # Generate nativefunction declarations
    # Note, eager registrations is set to False for the lazy TS backend as another LTC backend
    # may want to register their own lazy kernels instead of registering the TS ones.
    # The registration will lazily happen when init_ts_backend is called.
    gen_dispatchkey_nativefunc_headers(
        fm,
        class_name,
        cpp_namespace,
        backend_indices,
        grouped_native_functions,
        backend_key,
        autograd_key,
        backend_name,
    )

    # Generate Dispatcher registrations which hook up the nativefunctions
    for dispatch_key in (
        [backend_key] if autograd_key is None else [backend_key, autograd_key]
    ):
        gen_dispatcher_registrations(
            fm,
            output_dir,
            class_name,
            backend_indices,
            grouped_native_functions,
            backend_key,
            dispatch_key,
            selector,
            build_in_tree=build_in_tree,
            per_operator_headers=per_operator_headers,
            backend_name=backend_name,
            eager_registration=False,
        )

    # Generate native function impls that build IR nodes
    ns_helper = NamespaceHelper(cpp_namespace)
    fm.write_with_template(
        f"{backend_key}NativeFunctions.cpp",
        "DispatchKeyNativeFunctions.cpp",
        lambda: {
            "includes": [
                f"#include <{path}>"
                for path in [
                    tensor_class_hdr,
                    shape_inference_hdr,
                    "ATen/Functions.h",
                    "ATen/native/TensorConversions.h",
                    "ATen/NativeFunctions.h",
                    "ATen/CompositeExplicitAutogradNonFunctionalFunctions.h",
                    "ATen/MetaFunctions.h",
                    "ATen/Operators.h",
                    "ATen/native/CPUFallback.h",
                    "torch/csrc/lazy/core/ir_builder.h",
                    "torch/csrc/lazy/core/lazy_graph_executor.h",
                    "torch/csrc/lazy/core/metrics.h",
                    "torch/csrc/lazy/core/shape.h",
                    f"{output_dir}/{backend_key}NativeFunctions.h",
                    f"{output_dir}/LazyIr.h",
                ]
                + (
                    ["torch/csrc/lazy/ts_backend/ts_eager_fallback.h"]
                    if gen_forced_fallback_code
                    else []
                )
            ],
            "helper_fns": get_ltc_helper_fns(),
            "native_functions_include": "",
            "namespace_prologue": ns_helper.prologue,
            "namespace_epilogue": ns_helper.epilogue,
            "native_function_definitions": list(
                concat_map_codegen(
                    native_func_definition_generator(
                        f"{backend_key}NativeFunctions",
                        backend_indices[backend_key],
                        tensor_class,
                        gen_forced_fallback_code,
                        backend_namespace,
                        get_tensorlist,
                        get_tensor_or_wrap_number,
                        try_get_tensor,
                        metrics_counter,
                        create_tensor,
                        create_from_first_tensor,
                        create_aten_from_ltc_tensor,
                        tuple_aten_from_ltc_tensors,
                        lazy_tensor_ptr,
                        get_device_fn,
                    ),
                    grouped_native_functions,
                )
            ),
        },
    )
    # Generate IR node classes
    lazy_ir_obj = lazy_ir_generator(
        backend_indices[backend_key], backend_name, node_base, use_lazy_shape
    )

    fm.write_with_template(
        "LazyIr.h",
        "LazyIr.h",
        lambda: {
            "lazy_ir_sysinc": [
                f"#include <{path}>"
                for path in [
                    "ATen/core/Formatting.h",
                    "c10/core/ScalarType.h",
                    "torch/csrc/lazy/core/hash.h",
                    "torch/csrc/lazy/core/ir.h",
                    "torch/csrc/lazy/core/shape.h",
                    "optional",
                    "vector",
                ]
            ],
            "lazy_ir_inc": [f'#include "{node_base_hdr}"']
            if node_base_hdr is not None
            else [],
            "ir_declarations": list(
                concat_map_codegen(
                    lazy_ir_obj, grouped_native_functions, full_codegen + ir_gen
                )
            ),
            "namespace_prologue": ns_helper.prologue,
            "namespace_epilogue": ns_helper.epilogue,
        },
    )

    # Generate Non Native IR Node classes
    fm.write_with_template(
        "LazyNonNativeIr.h",
        "LazyNonNativeIr.h",
        lambda: {
            "lazy_non_native_ir_inc": [
                f"#include <{path}>"
                for path in [
                    "torch/csrc/lazy/core/ir.h",
                    "torch/csrc/lazy/core/ir_builder.h",
                    "torch/csrc/lazy/core/internal_ops/ltc_ops.h",
                    "torch/csrc/lazy/core/shape_inference.h",
                ]
                + ([node_base_hdr] if node_base_hdr else [])
                if path
            ],
            "non_native_ir_nodes": dest.generate_non_native_lazy_ir_nodes(
                non_native, lazy_ir_obj
            ),
            "namespace_prologue": ns_helper.prologue,
            "namespace_epilogue": ns_helper.epilogue,
        },
    )


if __name__ == "__main__":
    main()