pytorch/tools/autograd/gen_view_funcs.py

# Generates ViewFuncs.h/cpp
#
# NOTE: If any changes are being made to the ViewFunc codegen please also check
# if updates are needed in torch/csrc/autograd/autograd_not_implemented_fallback.cpp
# The fallback is expected to mimic this codegen, so we should keep the two in sync.

from typing import List, Tuple

import torchgen.api.dispatcher as dispatcher
from torchgen.api.autograd import NativeFunctionWithDifferentiabilityInfo
from torchgen.api.translate import translate
from torchgen.api.types import (
    BaseCType,
    Binding,
    NamedCType,
    SymIntT,
    tensorT,
    VectorCType,
)
from torchgen.code_template import CodeTemplate
from torchgen.model import Argument, NativeFunction, OptionalType
from torchgen.utils import FileManager

from .gen_inplace_or_view_type import (
    CALL_DISPATCH,
    extract_bindings,
    get_view_info,
    modifies_arguments,
    use_derived,
)

FUNCTION_DECLARATION = CodeTemplate(
    """\
#define ${uppercase_op}_AVAILABLE
struct ${op} : public ${superclass} {
  ${op}(${constructor_args}) ${initializer_list}
  {};
  virtual ~${op}() override {};
  virtual std::vector<c10::SymInt> get_symints() const override;
  virtual size_t num_symints() const override;
  virtual std::vector<at::Tensor> get_tensors() const override;
  virtual size_t num_tensors() const override;
  virtual at::Tensor operator()(const at::Tensor&) const override;
  virtual std::unique_ptr<ViewFunc> clone_and_set(
      std::optional<std::vector<c10::SymInt>> = c10::nullopt,
      std::optional<std::vector<at::Tensor>> = c10::nullopt) const override;

protected:
  virtual void set_symints(std::vector<c10::SymInt>) override;
  virtual void set_tensors(std::vector<at::Tensor>) override;

private:
  ${state}
};

"""
)

FUNCTION_DEFINITION = CodeTemplate(
    """\
std::vector<c10::SymInt> ${op}::get_symints() const {
  ${get_symints}
}

size_t ${op}::num_symints() const {
  return static_cast<size_t>(${num_symints});
}

void ${op}::set_symints(std::vector<c10::SymInt> ${symints_vec}) {
  TORCH_INTERNAL_ASSERT(${symints_vec}.size() == num_symints());
  ${set_symints}
}

std::vector<at::Tensor> ${op}::get_tensors() const {
  ${get_tensors}
}

size_t ${op}::num_tensors() const {
  return static_cast<size_t>(${num_tensors});
}

void ${op}::set_tensors(std::vector<at::Tensor> ${tensors_vec}) {
  TORCH_INTERNAL_ASSERT(${tensors_vec}.size() == num_tensors());
  ${set_tensors}
}

at::Tensor ${op}::operator()(const at::Tensor& ${call_input_name}) const {
  return ${op_call};
}

std::unique_ptr<ViewFunc> ${op}::clone_and_set(
    std::optional<std::vector<c10::SymInt>> ${symints_vec},
    std::optional<std::vector<at::Tensor>> ${tensors_vec}) const {
  auto output = std::make_unique<${op}>(${clone_args});
  if (${symints_vec}.has_value()) {
    output->set_symints(std::move(*(${symints_vec})));
  }
  if (${tensors_vec}.has_value()) {
    output->set_tensors(std::move(*(${tensors_vec})));
  }
  return output;
}

"""
)


# e.g. as_strided -> AsStridedViewFunc for camel case or
# as_strided_view_func otherwise
def view_func_name(
    f: NativeFunction, include_namespace: bool = False, camel_case: bool = True
) -> str:
    name = f.func.name.unambiguous_name()
    view_func_name = f"{name.replace('.', '_')}_view_func"
    if camel_case:
        is_private = view_func_name.startswith("_")
        view_func_name = "".join(
            [p.title() for p in view_func_name.replace(".", "_").split("_")]
        )
        if is_private:
            # put the leading underscore back in
            view_func_name = f"_{view_func_name}"
    namespace = "torch::autograd::generated::" if include_namespace else ""
    return f"{namespace}{view_func_name}"


def is_symint_or_tensor(arg: Argument) -> bool:
    return arg.type.is_tensor_like() or arg.type.is_symint_like()


def remove_const_ref(binding: Binding) -> Binding:
    return Binding(
        name=binding.name,
        nctype=binding.nctype.remove_const_ref(),
        argument=binding.argument,
        default=binding.default,
    )


def returns_multi_tensor(fn: NativeFunction) -> bool:
    returns = fn.func.returns
    assert len(returns) == 1
    returns_list_like = returns[0].type.is_list_like() is not None
    returns_tensor_like = returns[0].type.is_tensor_like()
    return returns_list_like and returns_tensor_like


# Generates strings with logic for getting / setting state of a particular type.
#
# Args:
#   bindings (list): List of state bindings of interest (may be empty)
#   state_vec_type (NamedCType): Type of vector to either return or copy from
#
# Returns:
#   tuple: (list of getter logic strings, list of setter logic strings, string
#     with num items expression)
def generate_state_getter_setter(
    bindings: List[Binding],
    state_vec_type: NamedCType,
) -> Tuple[List[str], List[str], str]:
    getter_logic = []
    setter_logic = []

    state_vec = state_vec_type.name
    getter_logic.append(f"{state_vec_type.cpp_type()} {state_vec};")
    if len(bindings) > 0:
        setter_logic.append("auto i = 0;")

    num_exprs = []
    for i, b in enumerate(bindings):
        assert isinstance(b.argument, Argument)
        if b.argument.type.is_list_like():
            # Handle list-likes.
            num_expr = f"{b.name}.size()"
            num_exprs.append(num_expr)
            getter = f"{state_vec}.insert({state_vec}.end(), {b.name}.begin(), {b.name}.end());"
            setter = f"std::copy({state_vec}.begin() + i, {state_vec}.begin() + i + {b.name}.size(), {b.name}.begin());"
        elif isinstance(b.argument.type, OptionalType):
            # Handle optionals.
            num_expr = f"({b.name}.has_value() ? 1 : 0)"
            num_exprs.append(num_expr)
            conditional = f"if({b.name}.has_value())"
            getter = (
                f"{conditional} {state_vec}.insert({state_vec}.end(), *({b.name}));"
            )
            setter = f"{conditional} {b.name} = {state_vec}[i];"
        else:
            num_expr = "1"
            num_exprs.append(num_expr)
            getter = f"{state_vec}.push_back({b.name});"
            setter = f"{b.name} = {state_vec}[i];"

        getter_logic.append(getter)
        setter_logic.append(setter)
        if i < len(bindings) - 1:
            setter_logic.append(f"i += {num_expr};")

    # Reserve / assert based on the total number of items expression.
    num_items = "0" if len(num_exprs) == 0 else " + ".join(num_exprs)
    if len(bindings) > 0:
        getter_logic.insert(1, f"{state_vec}.reserve({num_items});")

    getter_logic.append(f"return {state_vec};")

    return getter_logic, setter_logic, num_items


def process_function(fn: NativeFunction, template: CodeTemplate) -> str:
    bindings = extract_bindings(fn)
    non_self_bindings = [b for b in bindings if b.name != "self"]

    non_self_args = fn.func.arguments.flat_all[1:]
    non_self_value_bindings = [
        dispatcher.argument(a, remove_non_owning_ref_types=True) for a in non_self_args
    ]

    # Generate constructor / clone args for the generated struct.
    constructor_args = [b.defn() for b in non_self_bindings]
    clone_args = [b.name for b in non_self_bindings]

    # Generate state variable declarations for the generated struct.
    state_variables = [
        f"{remove_const_ref(b).defn()};" for b in non_self_value_bindings
    ]

    # Generate initializer list expressions for the generated struct.
    # allow_expensive_conversions=True because we need to store e.g. SymIntArrayRefs as
    # vector<SymInt>s.
    init_exprs = translate(
        non_self_bindings, non_self_value_bindings, allow_expensive_conversions=True
    )
    initializers = []
    for b, init_expr in zip(non_self_bindings, init_exprs):
        name = b.nctype.name
        assert isinstance(name, str)
        initializers.append(f"{name}({init_expr.expr})")

    # Generate call to underlying view op
    call_input_name = "input_base"
    op_call_args = [call_input_name, *(b.name for b in non_self_bindings)]
    op_call = CALL_DISPATCH.substitute(
        unambiguous_name=fn.func.name.unambiguous_name(),
        unpacked_args=op_call_args,
    )

    # Multi-output views additionally require a view_idx for disambiguation.
    if returns_multi_tensor(fn):
        view_idx_name = "view_idx"
        view_idx_typename = "int64_t"
        view_idx_decl = f"{view_idx_typename} {view_idx_name}"
        constructor_args.append(view_idx_decl)
        clone_args.append(view_idx_name)
        state_variables.append(f"{view_idx_decl};")
        initializers.append(f"{view_idx_name}({view_idx_name})")
        op_call += f"[{view_idx_name}]"

    # Generate initializer list for the generated struct.
    initializer_list = f": {', '.join(initializers)}" if len(initializers) > 0 else ""

    # Generate getter / setter logic for any symints.
    symint_bindings = [
        b
        for b in non_self_bindings
        if isinstance(b.argument, Argument) and b.argument.type.is_symint_like()
    ]
    symints_vec_type = NamedCType("symints", VectorCType(BaseCType(SymIntT)))
    get_symints, set_symints, num_symints = generate_state_getter_setter(
        symint_bindings, symints_vec_type
    )

    # Generate getter / setter logic for any tensors.
    tensor_bindings = [
        b
        for b in non_self_bindings
        if isinstance(b.argument, Argument) and b.argument.type.is_tensor_like()
    ]
    tensors_vec_type = NamedCType("tensors", VectorCType(BaseCType(tensorT)))
    get_tensors, set_tensors, num_tensors = generate_state_getter_setter(
        tensor_bindings, tensors_vec_type
    )

    return template.substitute(
        op=view_func_name(fn),
        uppercase_op=view_func_name(fn, camel_case=False).upper(),
        superclass="torch::autograd::ViewFunc",
        initializer_list=initializer_list,
        state=state_variables,
        constructor_args=constructor_args,
        clone_args=clone_args,
        symints_vec=symints_vec_type.name,
        get_symints=get_symints,
        set_symints=set_symints,
        num_symints=num_symints,
        tensors_vec=tensors_vec_type.name,
        get_tensors=get_tensors,
        set_tensors=set_tensors,
        num_tensors=num_tensors,
        call_input_name=call_input_name,
        op_call=op_call,
    )


def gen_view_funcs(
    out: str,
    fns_with_infos: List[NativeFunctionWithDifferentiabilityInfo],
    template_path: str,
) -> None:
    # don't need the info parts, just the function
    fns = [fn.func for fn in fns_with_infos if use_derived(fn)]
    # only want out-of-place views
    view_fns = [
        fn for fn in fns if get_view_info(fn) is not None and not modifies_arguments(fn)
    ]

    declarations = [process_function(fn, FUNCTION_DECLARATION) for fn in view_fns]
    definitions = [process_function(fn, FUNCTION_DEFINITION) for fn in view_fns]
    ops_headers = [f"#include <ATen/ops/{fn.root_name}_ops.h>" for fn in view_fns]

    file_basename = "ViewFuncs"
    fm = FileManager(install_dir=out, template_dir=template_path, dry_run=False)
    for suffix in [".h", ".cpp"]:
        fname = file_basename + suffix
        fm.write_with_template(
            fname,
            fname,
            lambda: {
                "generated_comment": "@"
                + f"generated from {fm.template_dir_for_comments()}/"
                + fname,
                "view_func_declarations": declarations,
                "view_func_definitions": definitions,
                "ops_headers": ops_headers,
            },
        )