1510 lines
57 KiB
Python
1510 lines
57 KiB
Python
from dataclasses import dataclass
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from typing import Dict, List, Optional, Sequence, Set, Tuple, Union
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from torchgen.api import cpp
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from torchgen.api.types import Binding, CppSignature, CppSignatureGroup
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from torchgen.gen import pythonify_default
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from torchgen.model import (
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Argument,
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BaseTy,
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BaseType,
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FunctionSchema,
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ListType,
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NativeFunction,
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OptionalType,
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Return,
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Type,
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Variant,
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)
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
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#
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# Data Models
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#
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
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#
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# [Notes] python binding codegen
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#
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# The Python binding codegen produces code that takes the input list of
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# PyObjects, finds the matching ATen C++ function using PythonArgParser,
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# converts the PyObjects into C++ types and calls the ATen C++ function:
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#
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# +--------+ parsing +------------------------+ binding +-----------------------+
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# | PyObjs | ---------> | PythonArgParser Output | ---------> | Cpp Function Dispatch |
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# +--------+ +------------------------+ +-----------------------+
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#
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# The following examples demonstrate the data models the Python binding
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# codegen needs to deal with and the tasks it needs to accomplish. It
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# helps understand the purpose of the new data types we introduced below.
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#
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# - Function Schema (source of truth)
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#
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# aten::empty.names(int[] size, *, Dimname[]? names,
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# ScalarType? dtype=None, Layout? layout=None,
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# Device? device=None, bool? pin_memory=None,
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# MemoryFormat? memory_format=None) -> Tensor
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#
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# - Python Signature
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#
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# It's used to generate input schema string for PythonArgParser.
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# Note: TensorOptions fields are reordered and the additional
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# 'requires_grad' field is added:
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#
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# empty(IntArrayRef size, *, DimnameList? names,
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# MemoryFormat? memory_format=None, ScalarType dtype=None,
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# Layout layout=torch.strided, Device device=None,
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# bool pin_memory=False, bool requires_grad=False)
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#
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# - C++ Signature
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#
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# It's used to generate C++ lambda formals & dispatch call.
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# Note: the scattered TensorOptions fields are packed into 'options'.
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#
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# auto dispatch_empty =
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# [](IntArrayRef size, c10::optional<DimnameList> names,
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# const TensorOptions & options,
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# c10::optional<MemoryFormat> memory_format) -> Tensor {
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# pybind11::gil_scoped_release no_gil;
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# return torch::empty(size, names, options, memory_format);
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# };
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#
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# - Binding between Python Arguments and C++ Arguments
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#
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# Given a set of Python Arguments in scope, we need produce the
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# binding expressions that translate the Python API into C++ API:
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#
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# Python Args Cpp Args Binding Exprs
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# -----------------------------------------------------------------
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# 0: size size '_r.intlist(0)'
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# 1: names names 'names' [special init]
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# 2: memory_format -------+
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# 3: dtype -----+-|--> options 'options' [special packing]
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# 4: layout / |
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# 5: device / +--> memory_format '_r.memoryformatOptional(2)'
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# 6: pin_memory /
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# 7: requires_grad -+
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#
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# So the full dispatch expression would look like:
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#
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# dispatch_empty(_r.intlist(0), names, options,
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# _r.memoryformatOptional(2))
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#
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# Where does 'names' come from? It involves special local init:
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#
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# auto __names = _r.toDimnameListOptional(1);
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# c10::optional<DimnameList> names =
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# __names ? c10::make_optional(DimnameList(__names.value()))
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# : c10::nullopt;
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#
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# Where does 'options' come from? It involves special local init
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# for TensorOptions. Note that Python side has the additional
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# 'requires_grad' field:
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#
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# const auto options = TensorOptions()
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# .dtype(_r.scalartype(3))
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# .device(_r.device(5))
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# .layout(_r.layoutOptional(4))
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# .requires_grad(_r.toBool(7))
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# .pinned_memory(_r.toBool(6));
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#
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# In some other cases one Python Argument can map to multiple C++
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# Arguments. For example:
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#
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# aten::max.names_dim(Tensor self, Dimname dim, bool keepdim=False)
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# -> (Tensor values, Tensor indices)
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#
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# Python Args Cpp Args Binding Exprs
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# ---------------------------------------------------------------------
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# +----> max 'out[0]'
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# /-----> max_values 'out[1]
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# 0: input / self '_r.tensor(0)'
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# 1: dim / dim '_r.dimname(1)'
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# 2: keepdim / keepdim '_r.toBool(2)'
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# 3: out -----+ [local init] out '_r.tensorlist_n<2>(3)'
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#
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# As demonstrated above, the binding can involve reordering,
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# packing, unpacking and special local inits.
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#
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#
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# Let's look at a concrete example:
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#
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# static PythonArgParser parser({
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# "abs(Tensor input, *, Tensor out=None)",
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ^
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# +--- Python Schema, represented by PythonSignature and PythonArgument
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#
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# }, /*traceable=*/true);
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#
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# ParsedArgs<2> parsed_args;
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# auto _r = parser.parse(nullptr, args, kwargs, parsed_args);
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#
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# ...
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#
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# if (_r.isNone(1)) {
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# ~~~~~~~~~~~~ <--- Scattered PythonArgParser output (arg name = 'out')
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# represented by PythonArgParserOutputExpr
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#
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# // aten::abs(Tensor self) -> Tensor
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ^
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# +--- NativeFunction schema, base version
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#
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# auto dispatch_abs = [](const Tensor & self) -> Tensor {
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ^
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# +--- dispatch_lambda_args / dispatch_lambda_return_str
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# generated from NativeFunction / CppSignature
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# (deprecated PythonSignature is special)
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# arguments are represented by DispatchLambdaArgument
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#
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# pybind11::gil_scoped_release no_gil;
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# return self.abs();
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# ~~~~~~~~~~~ <--- cpp_dispatch_target / cpp_dispatch_exprs
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# generated from NativeFunction / CppSignature
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# };
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# return wrap(dispatch_abs(_r.tensor(0)));
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# ~~~~~~~~~~~~~
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# ^
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# +--- dispatch_lambda_exprs
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# binding PythonArgParserOutputExpr (python args)
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# and DispatchLambdaArgument (c++ args)
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#
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# } else {
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# // aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ^
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# +--- NativeFunction schema, out-variant
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#
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# auto dispatch_abs_out = [](Tensor out, const Tensor & self) -> Tensor {
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# pybind11::gil_scoped_release no_gil;
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# return at::abs_out(out, self);
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# };
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# return wrap(dispatch_abs_out(_r.tensor(1), _r.tensor(0)));
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# }
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#
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#
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# [Notes] python interface codegen
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# The python dataclasses below are used used to generate both python binding code
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# and pyi type hint signatures.
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# In theory these two should look very similar, but there are number of differences
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# in how pyi signatures vs. python_arg_parser signatures are generated.
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# These differences have been encapsulated in signature_str() vs. signature_str_pyi()
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# to display the full signatures, and argument_str() vs argument_str_pyi() to display arguments.
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# For examples, only pyi signatures include return types.
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@dataclass(frozen=True)
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class PythonReturns:
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returns: Tuple[Return, ...]
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@dataclass(frozen=True)
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class PythonArgument:
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name: str
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type: Type
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default: Optional[str]
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# Used to generate the default init expr for some PythonArgParser outputs, e.g.:
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#
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# _r.layoutWithDefault(3, layout_from_backend(self.options().backend())))
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ^
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# +--- default_init str
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default_init: Optional[str]
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# Compute argument formal for python argument parsing.
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# Needs to be consistent with torch/csrc/utils/python_arg_parser.h.
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def argument_str(self, *, method: bool = False, symint: bool = True) -> str:
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type_str = (
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argument_type_str(self.type, symint=symint)
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.replace("const ", "")
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.replace(" &", "")
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)
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name = self.name
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# s/self/input/ outside method bindings
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# [old codegen] TODO: remove this? doesn't rename in codegen, it's just
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# for the parse string
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if name == "self" and type_str in ["Tensor", "Number"] and not method:
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name = "input"
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# add default
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if self.default is not None:
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default = {
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"nullptr": "None",
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"c10::nullopt": "None",
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"{}": "None",
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}.get(self.default, self.default)
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return f"{type_str} {name}={default}"
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else:
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return f"{type_str} {name}"
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def argument_str_pyi(
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self, *, method: bool = False, deprecated: bool = False
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) -> str:
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type_str = argument_type_str_pyi(self.type)
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name = self.name
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# s/self/input/ outside method bindings
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# [old codegen] TODO: remove this? doesn't rename in codegen, it's just
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# for the parse string
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if name == "self" and type_str == "Tensor" and not method and not deprecated:
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name = "input"
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if name == "from": # from is a Python keyword...
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name += "_"
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# pyi merges the _out and functional variants into the same signature, with an optional out arg
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if name == "out" and type_str == "Tensor" and not deprecated:
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type_str = "Optional[" + type_str + "]"
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# pyi deprecated signatures don't get defaults for their out arg
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treat_as_no_default = (
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deprecated
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and isinstance(self, PythonOutArgument)
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and self.default == "None"
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)
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# add default
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if self.default is not None and not treat_as_no_default:
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if (
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isinstance(self.type, ListType)
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and self.type.elem == BaseType(BaseTy.int)
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and self.default.startswith("{")
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and self.default.endswith("}")
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):
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default = "(" + self.default[1:-1] + ")"
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else:
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default = {
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"nullptr": "None",
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"c10::nullopt": "None",
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"{}": "None",
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"MemoryFormat::Contiguous": "contiguous_format",
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"QScheme::PER_TENSOR_AFFINE": "per_tensor_affine",
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}.get(self.default, self.default)
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return f"{name}: {type_str} = {default}"
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else:
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return f"{name}: {type_str}"
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@dataclass(frozen=True)
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class PythonOutArgument(PythonArgument):
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# In Python signature multiple output fields are packed into one 'out' argument.
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# When binding to C++, it's first binded to a local 'out' variable:
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# 'auto out = _r.tensorlist_n<2>(2);',
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# then binded to scattered C++ output arguments as 'out[0]', 'out[1]', and etc.
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# TODO: maybe don't need keep scattered out fields for python signature?
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outputs: Tuple[PythonArgument, ...]
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@staticmethod
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def from_outputs(
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outputs: Tuple[PythonArgument, ...]
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) -> Optional["PythonOutArgument"]:
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if not outputs:
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return None
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size = len(outputs)
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if size == 1:
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return PythonOutArgument(
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name=outputs[0].name,
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type=outputs[0].type,
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default="None",
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default_init=None,
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outputs=outputs,
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)
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elif size > 1:
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if any(not a.type.is_tensor_like() for a in outputs):
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raise RuntimeError(f"Unsupported output type: {outputs}")
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return PythonOutArgument(
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name="out",
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# TODO: shouldn't this be OptionalType[ListType[...]], since it defaults to None?
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type=ListType(BaseType(BaseTy.Tensor), size),
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default="None",
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default_init=None,
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outputs=outputs,
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)
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raise AssertionError(r"Unexpected PythonOutArgument size")
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@dataclass(frozen=True)
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class PythonSignature:
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# Base operator name, without inplace/outplace suffix.
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name: str
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# Positional arguments.
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# TODO: create a dedicated SelfArgument type for 'self'?
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input_args: Tuple[PythonArgument, ...]
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# Keyword arguments excluding the 'out' argument and scattered kwargs belonging
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# to TensorOptions (dtype, layout, device, pin_memory, requires_grad, etc).
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input_kwargs: Tuple[PythonArgument, ...]
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output_args: Optional[PythonOutArgument]
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# Return types, which are only used by pyi
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returns: PythonReturns
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# These are scattered kwargs arguments belonging to TensorOptions.
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# When binding to C++, they are packed into a TensorOptions object 'options'.
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# It's possible that the C++ signature doesn't take TensorOptions object (e.g.
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# for out variant), in which case they will be used as scattered fields without
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# being packed into 'options'.
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# TODO: maybe create a PythonTensorOptionsArgument?
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tensor_options_args: Tuple[PythonArgument, ...]
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# method or function signature?
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method: bool
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@property
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def deprecated(self) -> bool:
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return False
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def arguments(
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self, *, skip_outputs: bool = False, skip_tensor_options: bool = False
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) -> Tuple[Union[PythonArgument, PythonOutArgument], ...]:
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result: List[Union[PythonArgument, PythonOutArgument]] = []
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result.extend(self.input_args)
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result.extend(self.input_kwargs)
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if self.output_args is not None and not skip_outputs:
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result.append(self.output_args)
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if not skip_tensor_options:
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result.extend(self.tensor_options_args)
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return tuple(result)
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def arguments_count(self) -> int:
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return len(self.arguments())
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def output_idx(self) -> int:
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return len(self.input_args) + len(self.input_kwargs)
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# [old codegen] Compute the Python function signature for argument parsing,
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# as specified in torch/csrc/utils/python_arg_parser.h. WARNING:
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# this is NOT the same type signature as specified by PEP 484
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# as understood by mypy; our format was independently developed
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# and has some quirks to make it more suitable specifically
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# for error parsing.
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#
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# For a translation to mypy-valid type signatures, see
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# signature_str_pyi().
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def signature_str(self, *, skip_outputs: bool = False, symint: bool = True) -> str:
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args = self.arguments(skip_outputs=skip_outputs)
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schema_formals: List[str] = [
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a.argument_str(method=self.method, symint=symint) for a in args
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]
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positional_argc = len(self.input_args)
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if len(schema_formals) > positional_argc:
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schema_formals.insert(positional_argc, "*")
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return f'{self.name}({", ".join(schema_formals)})'
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def signature_str_pyi(self, *, skip_outputs: bool = False) -> str:
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args = self.arguments(skip_outputs=skip_outputs)
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schema_formals: List[str] = [
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a.argument_str_pyi(method=self.method) for a in args
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]
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positional_argc = len(self.input_args)
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if len(schema_formals) > positional_argc:
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schema_formals.insert(positional_argc, "*")
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# only pyi signatures include returns
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returns_str = returns_str_pyi(self)
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# pyi also includes self (with no typing/defaults) for methods
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if self.method:
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schema_formals.insert(0, "self")
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return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...'
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def signature_str_pyi_vararg(self, *, skip_outputs: bool = False) -> Optional[str]:
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# only pyi uses vararg signatures
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args = self.arguments(skip_outputs=skip_outputs)
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schema_formals: List[str] = [
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a.argument_str_pyi(method=self.method) for a in args
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]
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# vararg only applies to pyi signatures. vararg variants are not generated for all signatures
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num_args = self.arguments_count()
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num_positionalargs = len(self.input_args)
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have_vararg_version = False
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if num_args > 0:
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vararg_type = args[0].type
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if (
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isinstance(vararg_type, ListType)
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and str(vararg_type.elem) in ["int", "SymInt"]
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and num_positionalargs == 1
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):
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have_vararg_version = True
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if not have_vararg_version:
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return None
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# Below are the major changes in vararg vs. regular pyi signatures
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# vararg signatures also omit the asterix
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schema_formals[0] = "*" + args[0].name + ": _int"
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returns_str = returns_str_pyi(self)
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# pyi also includes self (with no typing/defaults) for methods
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if self.method:
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schema_formals.insert(0, "self")
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return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...'
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# The deprecated python signature involves some special logic, so create a
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# dedicated data model to store these extra properties.
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@dataclass(frozen=True)
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class PythonSignatureDeprecated(PythonSignature):
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# Schema for the deprecated function
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deprecated_schema: FunctionSchema
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# The deprecated signature might miss some arguments that the corresponding
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# C++ signature expects. We need store the constant default values to pass in.
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# For example:
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# [deprecate signature]: addmm(Scalar beta, Tensor self, Tensor mat1, Tensor mat2)
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# [func schema]: aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
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# [func call]: self.addmm(mat1, mat2, beta, 1)
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# We store ['self', 'mat1', 'mat2', 'beta', '1'] in this case.
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deprecated_args_exprs: Tuple[str, ...]
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@property
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def deprecated(self) -> bool:
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return True
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def signature_str(self, *, skip_outputs: bool = False, symint: bool = True) -> str:
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return (
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PythonSignature.signature_str(
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self, skip_outputs=skip_outputs, symint=symint
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)
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+ "|deprecated"
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)
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def signature_str_pyi(self, *, skip_outputs: bool = False) -> str:
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args = self.arguments(skip_outputs=skip_outputs)
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schema_formals: List[str] = [
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a.argument_str_pyi(method=self.method, deprecated=True) for a in args
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]
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positional_argc = len(self.input_args)
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if len(schema_formals) > positional_argc:
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schema_formals.insert(positional_argc, "*")
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returns_str = returns_str_pyi(self)
|
|
return f'def {self.name}({", ".join(schema_formals)}) -> {returns_str}: ...'
|
|
|
|
def signature_str_pyi_vararg(self, *, skip_outputs: bool = False) -> Optional[str]:
|
|
# the codegen doesn't include vararg variants for deprecated signatures
|
|
return None
|
|
|
|
|
|
# This struct is used to hold the PythonSignature and its corresponding
|
|
# NativeFunction BEFORE grouping base and out-variant functions.
|
|
# Why not store NativeFunction in PythonSignature or construct PythonSignature
|
|
# from NativeFunction? Because they are not 1-1 mapped.
|
|
# One native function could have both deprecated and non-deprecated python
|
|
# signatures - NativeFunction doesn't contain information to construct the
|
|
# deprecated python signature.
|
|
# One python signature is used to handle both the base and the out-variant
|
|
# function - see 'PythonSignatureGroup'.
|
|
@dataclass(frozen=True)
|
|
class PythonSignatureNativeFunctionPair:
|
|
signature: PythonSignature
|
|
function: NativeFunction
|
|
|
|
|
|
# We merge pairs of functions with signatures that are equivalent mod
|
|
# output arguments, and use a single entry in the python_arg_parser sig
|
|
# list for both (output arguments become optional).
|
|
@dataclass(frozen=True)
|
|
class PythonSignatureGroup:
|
|
# The signature used for Python argument parsing. The outplace signature
|
|
# is preferred if exists, because it can be used to parse inputs for both
|
|
# the out-place variant and the base version (with output omitted).
|
|
signature: PythonSignature
|
|
|
|
# The regular ATen declaration (e.g. conv2d)
|
|
base: NativeFunction
|
|
|
|
# The out variant (e.g. conv2d_out)
|
|
outplace: Optional[NativeFunction]
|
|
|
|
@classmethod
|
|
def from_pairs(
|
|
cls,
|
|
functional: PythonSignatureNativeFunctionPair,
|
|
out: Optional[PythonSignatureNativeFunctionPair],
|
|
) -> "PythonSignatureGroup":
|
|
if out is None:
|
|
return PythonSignatureGroup(
|
|
signature=functional.signature,
|
|
base=functional.function,
|
|
outplace=None,
|
|
)
|
|
|
|
# prefer the signature with optional out=... arguments because it's the
|
|
# superset that can be used to parse input for both base and outplace.
|
|
signature_kwargs = out.signature.__dict__.copy()
|
|
|
|
# Out overloads in C++ don't have TensorOptions arguments,
|
|
# so take these from the functional variant
|
|
signature_kwargs[
|
|
"tensor_options_args"
|
|
] = functional.signature.tensor_options_args
|
|
|
|
return PythonSignatureGroup(
|
|
signature=type(out.signature)(**signature_kwargs),
|
|
base=functional.function,
|
|
outplace=out.function,
|
|
)
|
|
|
|
|
|
# C++ function dispatch is wrapped in a lambda function. The lambda function
|
|
# has almost the same signature as the C++ function, only with some small
|
|
# variants - see details below.
|
|
# This data model is used to represent arguments of the lambda function
|
|
# signature.
|
|
@dataclass(frozen=True)
|
|
class DispatchLambdaArgument:
|
|
name: str
|
|
type_str: str
|
|
is_out_arg: bool
|
|
|
|
|
|
# To pass PyObjects arguments to C++ function (via the lambda wrapper),
|
|
# we need first convert PyObjects into simple C++ objects. This work
|
|
# is done by PythonArgParser.
|
|
# This data model is used to represent the output of PythonArgParser.
|
|
# It has 1-1 mapping with PythonArgument in PythonSignature.
|
|
@dataclass(frozen=True)
|
|
class PythonArgParserOutputExpr:
|
|
# argument name
|
|
name: str
|
|
|
|
# RHS expression to reference PythonArgParser output.
|
|
expr: str
|
|
|
|
# In some special cases we need create different expr, e.g.:
|
|
# '_r.isNone(1)' instead of '_r.tensor(1)'.
|
|
index: int
|
|
|
|
# The python argument it maps to.
|
|
argument: PythonArgument
|
|
|
|
@property
|
|
def is_none_expr(self) -> str:
|
|
return f"_r.isNone({self.index})"
|
|
|
|
|
|
# To pass PythonArgParser output to the lambda wrapper, we need bind
|
|
# PythonArgParserOutputExpr to DispatchLambdaArgument.
|
|
# They are not always 1-1 mapped, e.g. scattered TensorOptions fields
|
|
# need be packed into a TensorOptions object, which is the argument
|
|
# that the lambda function wrapper takes.
|
|
@dataclass(frozen=True)
|
|
class DispatchLambdaArgumentExprs:
|
|
# The exprs that provide the binding for lambda arguments, e.g.:
|
|
#
|
|
# 'self' -> '_r.tensor(0)'
|
|
# 'min' -> 'out[0]' / 'min_indices' -> 'out[1]'
|
|
# 'options' -> 'options'
|
|
#
|
|
# It has 1-1 mapping with DispatchLambdaArgument.
|
|
exprs: Sequence[str]
|
|
|
|
# Special local inits, which might introduce new variables that
|
|
# the 'exprs' above reference, e.g.:
|
|
#
|
|
# 'auto out = _r.tensorlist_n<2>(2);'
|
|
#
|
|
inits: Sequence[str]
|
|
|
|
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
#
|
|
# Helper Functions
|
|
#
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
|
|
|
|
def _cpp_signature(f: NativeFunction, *, method: bool = False) -> CppSignature:
|
|
return CppSignatureGroup.from_native_function(f, method=method).signature
|
|
|
|
|
|
def has_tensor_options(f: NativeFunction) -> bool:
|
|
return f.func.arguments.tensor_options is not None
|
|
|
|
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
#
|
|
# Python Signature
|
|
#
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
|
|
|
|
# 'simple_type' was introduced by the old codegen, which is slightly
|
|
# different from the python schema type, e.g.: doesn't have '?' suffix
|
|
# for optional Tensor/TensorList; doesn't have '[size]' suffix for list type.
|
|
def argument_type_str(
|
|
t: Type, *, simple_type: bool = False, symint: bool = True
|
|
) -> str:
|
|
if isinstance(t, BaseType):
|
|
if t.name == BaseTy.Tensor:
|
|
return "Tensor"
|
|
elif t.name == BaseTy.int:
|
|
return "int64_t"
|
|
elif t.name == BaseTy.float:
|
|
return "double"
|
|
elif t.name == BaseTy.str:
|
|
return "c10::string_view"
|
|
elif t.name in [
|
|
BaseTy.bool,
|
|
BaseTy.QScheme,
|
|
BaseTy.Scalar,
|
|
BaseTy.ScalarType,
|
|
BaseTy.Generator,
|
|
BaseTy.Storage,
|
|
BaseTy.Layout,
|
|
BaseTy.Device,
|
|
BaseTy.DeviceIndex,
|
|
BaseTy.MemoryFormat,
|
|
BaseTy.Dimname,
|
|
BaseTy.Stream,
|
|
BaseTy.ConstQuantizerPtr,
|
|
BaseTy.SymInt,
|
|
]:
|
|
# These python schema type names line up with their function schema names
|
|
return t.name.name
|
|
|
|
elif isinstance(t, OptionalType):
|
|
if str(t.elem) == "Tensor":
|
|
# Is it desired to keep '?' for simple_type with new style dispatcher?
|
|
return "Tensor?"
|
|
elem = argument_type_str(t.elem, simple_type=simple_type, symint=symint)
|
|
return f"{elem}?"
|
|
elif isinstance(t, ListType):
|
|
size = t.size if not simple_type else None
|
|
if str(t.elem) == "bool":
|
|
assert t.size is not None
|
|
return f"::std::array<bool,{t.size}>"
|
|
elif str(t.elem) == "int":
|
|
return f"IntArrayRef[{size}]" if size is not None else "IntArrayRef"
|
|
elif str(t.elem) == "SymInt":
|
|
if symint:
|
|
return (
|
|
f"SymIntArrayRef[{size}]" if size is not None else "SymIntArrayRef"
|
|
)
|
|
else:
|
|
return f"IntArrayRef[{size}]" if size is not None else "IntArrayRef"
|
|
elif str(t.elem) == "Tensor":
|
|
return f"TensorList[{size}]" if size is not None else "TensorList"
|
|
elif str(t.elem) == "Scalar":
|
|
return f"ScalarList[{size}]" if size is not None else "ScalarList"
|
|
elif str(t.elem) == "Tensor?":
|
|
if simple_type:
|
|
return "c10::List<c10::optional<Tensor>>"
|
|
else:
|
|
return "const c10::List<c10::optional<Tensor>> &"
|
|
elif str(t.elem) == "Dimname":
|
|
return f"DimnameList[{size}]" if size is not None else "DimnameList"
|
|
elem = argument_type_str(t.elem, simple_type=simple_type, symint=symint)
|
|
return f"ArrayRef<{elem}>"
|
|
|
|
raise RuntimeError(f"unrecognized type {repr(t)}")
|
|
|
|
|
|
def argument_type_size(t: Type) -> Optional[int]:
|
|
l = t.is_list_like()
|
|
if l is not None and str(l.elem) != "bool":
|
|
return l.size
|
|
else:
|
|
return None
|
|
|
|
|
|
def argument(a: Argument) -> PythonArgument:
|
|
return PythonArgument(
|
|
name=a.name,
|
|
type=a.type,
|
|
# TODO: directly translate a.default to python default
|
|
default=str(
|
|
pythonify_default(cpp.default_expr(a.default, a.type, symint=False))
|
|
)
|
|
if a.default is not None
|
|
else None,
|
|
default_init=None,
|
|
)
|
|
|
|
|
|
# Generates a PythonSignature that can be used for either .pyi or PythonArgParser codegen
|
|
def signature(
|
|
f: NativeFunction, *, method: bool = False, pyi: bool = False
|
|
) -> PythonSignature:
|
|
return signature_from_schema(
|
|
f.func, category_override=f.category_override, method=method, pyi=pyi
|
|
)
|
|
|
|
|
|
def signature_from_schema(
|
|
func: FunctionSchema,
|
|
*,
|
|
category_override: Optional[str],
|
|
method: bool = False,
|
|
pyi: bool = False,
|
|
) -> PythonSignature:
|
|
args: List[Argument] = []
|
|
args.extend(func.arguments.pre_self_positional)
|
|
# Skip SelfArgument if this is method.
|
|
if not method and func.arguments.self_arg is not None:
|
|
args.append(func.arguments.self_arg.argument)
|
|
args.extend(func.arguments.post_self_positional)
|
|
args.extend(func.arguments.pre_tensor_options_kwarg_only)
|
|
# Skip TensorOptionsArguments. Python side TensorOptions
|
|
# arguments are created based on different rules - see below.
|
|
args.extend(func.arguments.post_tensor_options_kwarg_only)
|
|
args.extend(func.arguments.out)
|
|
|
|
input_arg_set = {a.name for a in func.arguments.flat_positional}
|
|
kwarg_only_set = {a.name for a in func.arguments.flat_kwarg_only}
|
|
out_arg_set = {a.name for a in func.arguments.out}
|
|
|
|
input_args = tuple(map(argument, filter(lambda a: a.name in input_arg_set, args)))
|
|
input_kwargs = tuple(
|
|
map(argument, filter(lambda a: a.name in kwarg_only_set, args))
|
|
)
|
|
outputs = tuple(map(argument, filter(lambda a: a.name in out_arg_set, args)))
|
|
|
|
# Reintroduce the scattered fields of TensorOptions for Python.
|
|
# Compared to the cpp counterpart, the python arguments have new property
|
|
# (default_init) and a new argument 'requires_grad', which require some
|
|
# special handlings.
|
|
# [old codegen] TODO: because these aren't guaranteed to be 100% faithful
|
|
# to the original versions in the yaml, this recreation is a potential
|
|
# source of drift between eager and JIT. Pull this logic out to a shared place.
|
|
|
|
has_tensor_input_arg = any(
|
|
a.type.is_tensor_like() for a in func.arguments.flat_non_out
|
|
)
|
|
if any(a.name == "requires_grad" for a in func.schema_order_arguments()):
|
|
raise ValueError(
|
|
"argument named requires_grad is reserved, should not explicitly add it in the schema"
|
|
)
|
|
|
|
# [old codegen] this probably won't work if one of the returns is not a tensor,
|
|
# but it will produce a compile-time error that is obvious.
|
|
has_tensor_return = any(r.type.is_tensor_like() for r in func.returns)
|
|
|
|
name: str = cpp.name(func)
|
|
is_factory_function = category_override == "factory" or (
|
|
has_tensor_return and not has_tensor_input_arg
|
|
)
|
|
is_like_or_new_function = (
|
|
category_override in ("new", "like")
|
|
or name.startswith("new_")
|
|
or name.endswith("_like")
|
|
)
|
|
is_dummy_function = category_override == "dummy"
|
|
|
|
tensor_options_args: List[PythonArgument] = []
|
|
if (is_factory_function or is_like_or_new_function) and not is_dummy_function:
|
|
|
|
def topt_default_init(name: str) -> Optional[str]:
|
|
topt_args = func.arguments.tensor_options
|
|
if topt_args is None:
|
|
return None
|
|
a = getattr(topt_args, name)
|
|
if a.default is None or a.default == "None":
|
|
return None
|
|
return cpp.default_expr(a.default, a.type, symint=False)
|
|
|
|
tensor_options_args.append(
|
|
PythonArgument(
|
|
name="dtype",
|
|
type=OptionalType(BaseType(BaseTy.ScalarType)),
|
|
default="None",
|
|
default_init=(
|
|
None if is_like_or_new_function else topt_default_init("dtype")
|
|
),
|
|
)
|
|
)
|
|
tensor_options_args.append(
|
|
PythonArgument(
|
|
name="layout",
|
|
type=OptionalType(BaseType(BaseTy.Layout)),
|
|
default="None",
|
|
default_init=(
|
|
None if is_like_or_new_function else topt_default_init("layout")
|
|
),
|
|
)
|
|
)
|
|
tensor_options_args.append(
|
|
PythonArgument(
|
|
name="device",
|
|
type=OptionalType(BaseType(BaseTy.Device)),
|
|
default="None",
|
|
default_init=(
|
|
None
|
|
if is_like_or_new_function
|
|
else (
|
|
topt_default_init("device")
|
|
or "torch::tensors::get_default_device()"
|
|
)
|
|
),
|
|
)
|
|
)
|
|
tensor_options_args.append(
|
|
PythonArgument(
|
|
name="pin_memory",
|
|
type=OptionalType(BaseType(BaseTy.bool)),
|
|
default="False",
|
|
default_init=None,
|
|
)
|
|
)
|
|
tensor_options_args.append(
|
|
PythonArgument(
|
|
name="requires_grad",
|
|
type=OptionalType(BaseType(BaseTy.bool)),
|
|
default="False",
|
|
default_init=None,
|
|
)
|
|
)
|
|
|
|
returns = PythonReturns(returns=func.returns)
|
|
|
|
return PythonSignature(
|
|
name=str(func.name.name),
|
|
input_args=input_args,
|
|
input_kwargs=input_kwargs,
|
|
output_args=PythonOutArgument.from_outputs(outputs),
|
|
tensor_options_args=tuple(tensor_options_args),
|
|
returns=returns,
|
|
method=method,
|
|
)
|
|
|
|
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
#
|
|
# Python Interface
|
|
#
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
|
|
|
|
def structseq_fieldnames(returns: Tuple[Return, ...]) -> List[str]:
|
|
if len(returns) <= 1 or all(r.name is None for r in returns):
|
|
return []
|
|
else:
|
|
if any(r.name is None for r in returns):
|
|
# When building on Windows, `PyStructSequence_UnnamedField` could not be
|
|
# resolved by the linker for some reason, which cause error in building:
|
|
#
|
|
# python_nn_functions.cpp.obj : error LNK2001: unresolved external symbol
|
|
# PyStructSequence_UnnamedField
|
|
#
|
|
# Thus, at this point in time, we do not support unnamed
|
|
# fields in structseq; you must either name all fields,
|
|
# or none of them.
|
|
raise ValueError("Unnamed field is not supported by codegen")
|
|
|
|
return [str(r.name) for r in returns]
|
|
|
|
|
|
def argument_type_str_pyi(t: Type) -> str:
|
|
add_optional = False
|
|
if isinstance(t, OptionalType):
|
|
t = t.elem
|
|
add_optional = True
|
|
|
|
if isinstance(t, BaseType):
|
|
if t.name in [BaseTy.int, BaseTy.DeviceIndex]:
|
|
ret = "_int"
|
|
if t.name == BaseTy.SymInt:
|
|
ret = "Union[_int, SymInt]"
|
|
elif t.name == BaseTy.float:
|
|
ret = "_float"
|
|
elif t.name == BaseTy.str:
|
|
ret = "str"
|
|
elif t.name == BaseTy.Scalar:
|
|
ret = "Union[Number, _complex]"
|
|
elif t.name == BaseTy.ScalarType:
|
|
ret = "_dtype"
|
|
elif t.name == BaseTy.bool:
|
|
ret = "_bool"
|
|
elif t.name == BaseTy.QScheme:
|
|
ret = "_qscheme"
|
|
elif t.name == BaseTy.Layout:
|
|
ret = "_layout"
|
|
elif t.name == BaseTy.Device:
|
|
ret = "Optional[DeviceLikeType]"
|
|
elif t.name == BaseTy.MemoryFormat:
|
|
ret = "memory_format"
|
|
elif t.name == BaseTy.Dimname:
|
|
ret = "Union[str, ellipsis, None]"
|
|
elif t.name == BaseTy.Storage:
|
|
ret = "Union[Storage, UntypedStorage]"
|
|
elif t.name in [BaseTy.Tensor, BaseTy.Generator, BaseTy.Stream]:
|
|
# These python schema type names line up with their function schema names
|
|
ret = t.name.name
|
|
|
|
elif isinstance(t, ListType):
|
|
if str(t.elem) == "int":
|
|
ret = "Union[_int, _size]" if t.size is not None else "_size"
|
|
elif t.is_tensor_like():
|
|
# TODO: this doesn't seem right...
|
|
# Tensor?[] currently translates to Optional[Union[Tuple[Tensor, ...], List[Tensor]]]
|
|
# It should probably translate to Union[Tuple[Optional[Tensor], ...], List[Optional[Tensor]]]
|
|
if isinstance(t.elem, OptionalType):
|
|
add_optional = True
|
|
ret = (
|
|
"Union[Tensor, Tuple[Tensor, ...], List[Tensor]]"
|
|
if t.size is not None
|
|
else "Union[Tuple[Tensor, ...], List[Tensor]]"
|
|
)
|
|
elif str(t.elem) == "float":
|
|
ret = "Sequence[_float]"
|
|
elif str(t.elem) == "SymInt" and t.size is not None:
|
|
elem = argument_type_str_pyi(t.elem)
|
|
ret = f"Union[{elem}, Sequence[{elem}]]"
|
|
else:
|
|
elem = argument_type_str_pyi(t.elem)
|
|
ret = f"Sequence[{elem}]"
|
|
|
|
else:
|
|
raise RuntimeError(f"unrecognized type {repr(t)}")
|
|
|
|
if add_optional:
|
|
ret = "Optional[" + ret + "]"
|
|
|
|
return ret
|
|
|
|
|
|
def return_type_str_pyi(t: Type) -> str:
|
|
# Where arguments are open to accepting Union, return types should return
|
|
# concrete types
|
|
|
|
if isinstance(t, OptionalType):
|
|
inner = return_type_str_pyi(t.elem)
|
|
return f"Optional[{inner}]"
|
|
|
|
if isinstance(t, BaseType):
|
|
if t.name == BaseTy.Device:
|
|
return "_device"
|
|
elif t.name == BaseTy.Dimname:
|
|
ret = "Optional[str]"
|
|
else:
|
|
return argument_type_str_pyi(t)
|
|
|
|
if isinstance(t, ListType):
|
|
inner = return_type_str_pyi(t.elem)
|
|
return f"Tuple[{inner}, ...]"
|
|
|
|
return argument_type_str_pyi(t)
|
|
|
|
|
|
def returns_structseq_pyi(signature: PythonSignature) -> Optional[Tuple[str, str]]:
|
|
python_returns = [return_type_str_pyi(r.type) for r in signature.returns.returns]
|
|
structseq_name = signature.name
|
|
field_names = structseq_fieldnames(signature.returns.returns)
|
|
if field_names:
|
|
# These types are structseq objects which act like named NamedTuples, but
|
|
# the constructor acts like the constructor of tuple. Using typing.NamedTuple
|
|
# does not allow us to override __init__.
|
|
field_names_str = ", ".join(repr(name) for name in field_names)
|
|
seq_type = f"Tuple[{', '.join(python_returns)}]"
|
|
structseq_def_lines = [
|
|
f"class {structseq_name}({seq_type}):",
|
|
]
|
|
for name, typ in zip(field_names, python_returns):
|
|
structseq_def_lines.extend(
|
|
[
|
|
" @property",
|
|
f" def {name}(self) -> {typ}: ...",
|
|
]
|
|
)
|
|
structseq_def_lines.extend(
|
|
[
|
|
f" def __new__(cls, sequence: {seq_type}): ...",
|
|
f" n_fields: _int = {len(field_names)}",
|
|
f" n_sequeunce_fields: _int = {len(field_names)}",
|
|
" n_unnamed_fields: _int = 0",
|
|
" def __init_subclass__(cls) -> NoReturn: ... # prohibit subclassing",
|
|
"", # add an extra newline
|
|
]
|
|
)
|
|
structseq_def = "\n".join(structseq_def_lines)
|
|
# Example:
|
|
# structseq_def = (
|
|
# "class max(Tuple[Tensor, Tensor]):\n"
|
|
# " @property\n"
|
|
# " def values(self) -> Tensor: ...\n"
|
|
# " @property\n"
|
|
# " def indices(self) -> Tensor: ...\n"
|
|
# " def __new__(cls, sequence: Tuple[Tensor, Tensor]): ...\n"
|
|
# " n_fields: _int = 2",
|
|
# " n_sequeunce_fields: _int = 2",
|
|
# " n_unnamed_fields: _int = 0",
|
|
# " def __init_subclass__(cls) -> NoReturn: ... # prohibit subclassing",
|
|
# )
|
|
return structseq_name, structseq_def
|
|
return None
|
|
|
|
|
|
def returns_str_pyi(signature: PythonSignature) -> str:
|
|
field_names = structseq_fieldnames(signature.returns.returns)
|
|
if field_names:
|
|
return f"torch.return_types.{signature.name}"
|
|
|
|
python_returns = [return_type_str_pyi(r.type) for r in signature.returns.returns]
|
|
if len(python_returns) > 1:
|
|
return "Tuple[" + ", ".join(python_returns) + "]"
|
|
if len(python_returns) == 1:
|
|
return python_returns[0]
|
|
return "None"
|
|
|
|
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
#
|
|
# C++ Function Dispatch
|
|
#
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
# This section provides APIs to generate the code that does C++ function
|
|
# dispatch. The C++ function call is wrapped by a lambda function.
|
|
# For example:
|
|
#
|
|
# // aten::selu_(Tensor(a!) self) -> Tensor(a!)
|
|
# auto dispatch_selu_ = [](Tensor self) -> Tensor {
|
|
# pybind11::gil_scoped_release no_gil;
|
|
# return at::selu_(self);
|
|
# };
|
|
#
|
|
# The lambda function's signature follows the C++ signature in common
|
|
# cases, e.g.:
|
|
#
|
|
# // aten::add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
|
|
# [](const Tensor & self, const Tensor & other, Scalar alpha) -> Tensor
|
|
#
|
|
# For out variant the 'out' argument's type is changed from 'Tensor &'
|
|
# to 'Tensor'. It's because when calling the lambda it passes in the
|
|
# PythonArgParser output '_r.tensor(3)', which is stack allocated object
|
|
# and needs to pass by value. Also see comments in 'dispatch_lambda_return_str()'.
|
|
#
|
|
# // aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
|
|
# [](Tensor out, const Tensor & self, const Tensor & other, Scalar alpha) -> Tensor
|
|
#
|
|
# For multi-output case it can keep using reference type because the
|
|
# PythonArgParser output has been unpacked to local variables, e.g.:
|
|
#
|
|
# // aten::max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *,
|
|
# // Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)
|
|
# [](Tensor & max, Tensor & max_values, const Tensor & self, Dimname dim, bool keepdim) -> std::tuple<Tensor,Tensor>
|
|
#
|
|
# For deprecated python signature, it should follow deprecated python arg order.
|
|
# TODO: This is to keep same byte-for-byte result as the old codegen - maybe unnecessary?
|
|
|
|
|
|
def dispatch_lambda_args(
|
|
ps: PythonSignature, f: NativeFunction, symint: bool = True
|
|
) -> Tuple[DispatchLambdaArgument, ...]:
|
|
if isinstance(ps, PythonSignatureDeprecated):
|
|
schema = ps.deprecated_schema
|
|
else:
|
|
schema = f.func
|
|
|
|
# Start with cpp arguments - dispatch lambda signature always include 'self'
|
|
cpp_args = cpp.arguments(
|
|
arguments=schema.arguments,
|
|
faithful=False,
|
|
symint=symint,
|
|
method=False,
|
|
cpp_no_default_args=f.cpp_no_default_args,
|
|
)
|
|
out_args: Set[str] = {a.name for a in schema.arguments.out}
|
|
|
|
# Convert from cpp argument to lambda argument
|
|
def dispatch_lambda_arg(cpp_arg: Binding) -> DispatchLambdaArgument:
|
|
type_str = cpp_arg.type
|
|
is_out_arg = cpp_arg.name in out_args
|
|
if ps.method and cpp_arg.name == "self":
|
|
# For method's 'self', we can use 'const Tensor &' and simply ignore mutability!
|
|
type_str = "const at::Tensor &"
|
|
else:
|
|
# For other cases we need prevent dangling refs to temps (unless it's
|
|
# unpacked scattered output)
|
|
# The reason is explained in the comments above and in 'dispatch_lambda_return_str()'.
|
|
# TODO: avoid this special handling?
|
|
ensure_temp_safe = len(out_args) <= 1 or not is_out_arg
|
|
if ensure_temp_safe:
|
|
type_str = {
|
|
"at::Tensor &": "at::Tensor",
|
|
}.get(type_str, type_str)
|
|
return DispatchLambdaArgument(
|
|
name=cpp_arg.name,
|
|
type_str=type_str,
|
|
is_out_arg=is_out_arg,
|
|
)
|
|
|
|
return tuple(map(dispatch_lambda_arg, cpp_args))
|
|
|
|
|
|
# [old codegen] XXX: if you got here because of an assertion failure, it doesn't mean
|
|
# it's enough to just extend the list here. Before you do this, make sure
|
|
# to add an appropriate wrap() overload in torch/csrc/autograd/utils/wrap_outputs.h.
|
|
SUPPORTED_RETURN_TYPES = {
|
|
"at::Tensor",
|
|
"::std::tuple<at::Tensor,at::Tensor>",
|
|
"::std::tuple<at::Tensor,at::Tensor,at::Tensor>",
|
|
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor>",
|
|
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor>",
|
|
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor>",
|
|
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,int64_t>",
|
|
"::std::tuple<at::Tensor,at::Tensor,double,int64_t>",
|
|
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,int64_t>",
|
|
"::std::tuple<at::Tensor,at::Tensor,double,at::Tensor,int64_t>",
|
|
"::std::tuple<double,int64_t>",
|
|
"::std::tuple<at::Tensor,::std::vector<at::Tensor>>",
|
|
"::std::vector<at::Tensor>",
|
|
# Needed for flash attention forw/backward
|
|
"::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor>",
|
|
"at::Scalar",
|
|
"bool",
|
|
"int64_t",
|
|
"void*",
|
|
"void",
|
|
"at::QScheme",
|
|
"double",
|
|
"at::IntArrayRef",
|
|
"at::ScalarType",
|
|
"at::Stream",
|
|
}
|
|
|
|
|
|
def dispatch_lambda_return_str(f: NativeFunction) -> str:
|
|
# [old codegen] Remove type annotation (e.g. 'Tensor' rather than 'Tensor &')
|
|
# because the dispatch lambdas take mutable arguments *by value*, not
|
|
# by reference. If you then return a reference to such an argument, you
|
|
# will now have a pointer to a dangling stack entry. Not good.
|
|
#
|
|
# You want:
|
|
#
|
|
# auto dispatch_selu_ = [](Tensor self) -> Tensor { ...; return at::selu_(self); };
|
|
# ^^^^^^
|
|
#
|
|
# *not*
|
|
#
|
|
# auto dispatch_selu_ = [](Tensor self) -> Tensor& { ...; return at::selu_(self); };
|
|
# ^^^^^^^
|
|
#
|
|
# (NB: We can't make dispatch_selu_ take Tensor&, because the enclosing
|
|
# codegen looks like dispatch_selu_(_r.tensor(0)), and you can't take a
|
|
# mutable reference to temporary. Maybe we could assign it to a
|
|
# variable itself.)
|
|
returns_without_annotation = tuple(
|
|
Return(r.name, r.type, None) for r in f.func.returns
|
|
)
|
|
return_str = cpp.returns_type(returns_without_annotation, symint=True).cpp_type()
|
|
if return_str not in SUPPORTED_RETURN_TYPES:
|
|
raise RuntimeError(f"{f.func.name} returns unsupported type {return_str}")
|
|
return return_str
|
|
|
|
|
|
def cpp_dispatch_target(f: NativeFunction) -> str:
|
|
symint = f.func.has_symint()
|
|
name = cpp.name(f.func, symint_overload=symint)
|
|
if Variant.method in f.variants:
|
|
return f"self.{name}"
|
|
if Variant.function in f.variants:
|
|
if has_tensor_options(f) or f.func.name.name.base.endswith("_like"):
|
|
namespace = "torch"
|
|
else:
|
|
namespace = "at"
|
|
return f"{namespace}::{name}"
|
|
raise RuntimeError(f"could not dispatch, neither function nor method: {f.func}")
|
|
|
|
|
|
def cpp_dispatch_exprs(
|
|
f: NativeFunction,
|
|
*,
|
|
python_signature: Optional[PythonSignature] = None,
|
|
) -> Tuple[str, ...]:
|
|
cpp_args: Sequence[Binding] = _cpp_signature(f, method=False).arguments()
|
|
|
|
exprs: Tuple[str, ...] = tuple()
|
|
if not isinstance(python_signature, PythonSignatureDeprecated):
|
|
# By default the exprs are consistent with the C++ signature.
|
|
exprs = tuple(a.name for a in cpp_args)
|
|
else:
|
|
# For deprecated python signature we may need fill in some constants.
|
|
exprs = tuple(
|
|
filter(
|
|
lambda n: n != "out" or f.func.is_out_fn(),
|
|
python_signature.deprecated_args_exprs,
|
|
)
|
|
)
|
|
|
|
if Variant.method in f.variants:
|
|
exprs = tuple(filter("self".__ne__, exprs))
|
|
|
|
return exprs
|
|
|
|
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
#
|
|
# Python / C++ Args Binding
|
|
#
|
|
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
|
|
|
|
|
|
# We explicitly enumerate the PythonArgParser unpacking methods for all
|
|
# supported types. This might be more verbose than necessary, partially
|
|
# because of the irregularity of unpacking method naming, partially
|
|
# because we want to mimic the old codegen behavior - to reject
|
|
# unexpected and/or unsupported cases which the old codegen rejects.
|
|
# For certain cases it is intentionally more restrictive than necessary,
|
|
# e.g.: it doesn't accepts doublelist with definite size.
|
|
def arg_parser_unpack_method(
|
|
t: Type, default: Optional[str], default_init: Optional[str], *, symint: bool = True
|
|
) -> str:
|
|
has_default_init = default_init is not None
|
|
if has_default_init and str(t) not in (
|
|
"ScalarType?",
|
|
"ScalarType",
|
|
"Device",
|
|
"Device?",
|
|
"Layout",
|
|
"Layout?",
|
|
"bool",
|
|
"bool?",
|
|
):
|
|
raise RuntimeError(f"type '{t}' does not supported unpacking with default")
|
|
|
|
if isinstance(t, BaseType):
|
|
if t.name in [
|
|
BaseTy.Tensor,
|
|
BaseTy.Stream,
|
|
BaseTy.Storage,
|
|
BaseTy.Scalar,
|
|
BaseTy.Dimname,
|
|
]:
|
|
# These unpack methods line up with their schema names
|
|
return t.name.name.lower()
|
|
elif t.name == BaseTy.ScalarType:
|
|
return "scalartypeWithDefault" if has_default_init else "scalartype"
|
|
elif t.name == BaseTy.Device:
|
|
return "deviceWithDefault" if has_default_init else "device"
|
|
elif t.name == BaseTy.DeviceIndex:
|
|
return "toInt64"
|
|
elif t.name == BaseTy.int:
|
|
return "toInt64"
|
|
elif t.name == BaseTy.SymInt:
|
|
return "toSymInt" if symint else "toInt64"
|
|
elif t.name == BaseTy.bool:
|
|
return "toBoolWithDefault" if has_default_init else "toBool"
|
|
elif t.name == BaseTy.float:
|
|
return "toDouble"
|
|
elif t.name == BaseTy.str:
|
|
return "stringView"
|
|
elif t.name == BaseTy.Layout:
|
|
return "layoutWithDefault" if has_default_init else "layout"
|
|
elif t.name == BaseTy.MemoryFormat:
|
|
return "memoryformat"
|
|
|
|
elif isinstance(t, OptionalType):
|
|
if str(t.elem) == "Tensor":
|
|
return "optionalTensor"
|
|
elif str(t.elem) == "Generator":
|
|
return "generator"
|
|
elif str(t.elem) == "Dimname[]":
|
|
return "toDimnameListOptional"
|
|
elif not has_default_init and default in (None, "None", "c10::nullopt"):
|
|
# If default is None: append 'Optional' to elem's unpacking method
|
|
return (
|
|
arg_parser_unpack_method(t.elem, None, None, symint=symint) + "Optional"
|
|
)
|
|
else:
|
|
# Otherwise, load as underlying type with default
|
|
return arg_parser_unpack_method(
|
|
t.elem, default, default_init, symint=symint
|
|
)
|
|
|
|
elif isinstance(t, ListType):
|
|
if str(t.elem) == "Tensor":
|
|
# accept and use definite size
|
|
return f"tensorlist_n<{t.size}>" if t.size is not None else "tensorlist"
|
|
elif str(t.elem) == "Tensor?":
|
|
return "list_of_optional_tensors"
|
|
elif str(t.elem) == "Dimname":
|
|
# accept definite size
|
|
return "dimnamelist"
|
|
elif str(t.elem) == "int":
|
|
# accept definite size
|
|
return "intlist"
|
|
elif str(t.elem) == "float":
|
|
return "doublelist"
|
|
elif str(t.elem) == "SymInt":
|
|
# accept definite size
|
|
return "symintlist" if symint else "intlist"
|
|
elif str(t.elem) == "Scalar":
|
|
return "scalarlist"
|
|
raise RuntimeError(f"type '{t}' is not supported by PythonArgParser")
|
|
|
|
|
|
# Return RHS expression for python argument using PythonArgParser output.
|
|
# e.g. for arg name 'foo', arg type 'bool', arg_index = 2, returns '_r.toBool(2)'
|
|
def arg_parser_output_expr(
|
|
arg_index: int, a: PythonArgument, *, symint: bool = True
|
|
) -> PythonArgParserOutputExpr:
|
|
has_default = a.default_init is not None
|
|
unpack_method = arg_parser_unpack_method(
|
|
t=a.type, default=a.default, default_init=a.default_init, symint=symint
|
|
)
|
|
default = f", {a.default_init}" if has_default else ""
|
|
expr = f"_r.{unpack_method}({arg_index}{default})"
|
|
|
|
return PythonArgParserOutputExpr(
|
|
name=a.name,
|
|
expr=expr,
|
|
index=arg_index,
|
|
argument=a,
|
|
)
|
|
|
|
|
|
# Returns a map with key = arg_name and value = PythonArgParserOutputExpr.
|
|
def arg_parser_output_exprs(
|
|
ps: PythonSignature, f: NativeFunction, *, symint: bool = True
|
|
) -> Dict[str, PythonArgParserOutputExpr]:
|
|
return {
|
|
e.name: e
|
|
for i, a in enumerate(ps.arguments())
|
|
for e in (arg_parser_output_expr(i, a, symint=symint),)
|
|
}
|
|
|
|
|
|
# argument name to type for scattered tensor options fields
|
|
TENSOR_OPTIONS_FIELDS = {
|
|
"dtype": "ScalarType?",
|
|
"device": "Device?",
|
|
"layout": "Layout?",
|
|
"pin_memory": "bool?",
|
|
"requires_grad": "bool?",
|
|
}
|
|
|
|
|
|
# bind arg parser outputs (python args) with dispatch lambda arguments (c++ args).
|
|
def dispatch_lambda_exprs(
|
|
ps: PythonSignature, f: NativeFunction, *, symint: bool = True
|
|
) -> DispatchLambdaArgumentExprs:
|
|
# This method is to bind 'arg_parser_outputs' and 'lambda_args' by producing
|
|
# 'inits' and 'lambda_args_exprs' for each lambda argument using arg parser
|
|
# outputs.
|
|
arg_parser_outputs = arg_parser_output_exprs(ps, f, symint=symint)
|
|
lambda_args = dispatch_lambda_args(ps, f, symint=symint)
|
|
inits: List[str] = []
|
|
lambda_args_exprs: Dict[str, str] = {}
|
|
|
|
has_toptions = has_tensor_options(f)
|
|
|
|
# 1. special inits/unpacking to provide binding exprs for lambda arguments.
|
|
for a in ps.arguments(skip_tensor_options=True):
|
|
name = a.name
|
|
arg_parser_expr = arg_parser_outputs[a.name].expr
|
|
|
|
if has_toptions and name == "self":
|
|
# TODO: why this needs to be special case?
|
|
inits.extend(
|
|
[
|
|
f"auto self = {arg_parser_expr};",
|
|
]
|
|
)
|
|
lambda_args_exprs[name] = name
|
|
elif (
|
|
isinstance(a, PythonOutArgument)
|
|
and len(a.outputs) > 1
|
|
and f.func.is_out_fn()
|
|
):
|
|
inits.extend(
|
|
[
|
|
f"auto out = {arg_parser_expr};",
|
|
]
|
|
)
|
|
for i, out_arg in enumerate(a.outputs):
|
|
lambda_args_exprs[out_arg.name] = f"out[{i}]"
|
|
elif str(a.type) == "Dimname[]?":
|
|
# [old codegen]
|
|
# TODO: make this part of something more general, or get rid of it.
|
|
# optional<ArrayRef<T>> are special. The PythonArgParser returns an
|
|
# optional<vector<T>>, which cannot be implicitly converted to
|
|
# optional<ArrayRef<T>>. One needs to unwrap the optional and rewrap.
|
|
inits.extend(
|
|
[
|
|
f"auto __{name} = {arg_parser_expr};",
|
|
f"c10::optional<DimnameList> {name} = __{name} ? c10::make_optional(DimnameList(__{name}.value())) : c10::nullopt;", # noqa: B950
|
|
]
|
|
)
|
|
lambda_args_exprs[name] = name
|
|
else:
|
|
# default case - directly using PythonArgParser output expr
|
|
lambda_args_exprs[name] = arg_parser_expr
|
|
|
|
# method's self is passed directly to python binding, rather than parsed
|
|
if ps.method:
|
|
lambda_args_exprs["self"] = "self"
|
|
|
|
# 2. special packing/checking for TensorOptions.
|
|
tensor_options_args_names = [a.name for a in ps.tensor_options_args]
|
|
if has_toptions:
|
|
if f.func.is_out_fn():
|
|
raise RuntimeError(f"{f.func}: tensor options with output arg")
|
|
for a in ps.tensor_options_args:
|
|
if a.name not in TENSOR_OPTIONS_FIELDS:
|
|
raise RuntimeError(
|
|
f"{f.func}: unrecognized tensor options field '{a.name}' in python binding arguments"
|
|
)
|
|
if str(a.type) != TENSOR_OPTIONS_FIELDS.get(a.name):
|
|
raise RuntimeError(
|
|
f"{f.func}: unrecognized type '{str(a.type)}' for tensor options field '{a.name}'"
|
|
)
|
|
if not all(
|
|
a in tensor_options_args_names for a in TENSOR_OPTIONS_FIELDS.keys()
|
|
):
|
|
raise RuntimeError(
|
|
f"{f.func}: incomplete tensor options args: {tensor_options_args_names}"
|
|
)
|
|
|
|
inits.append(
|
|
f"""\
|
|
const auto options = TensorOptions()
|
|
.dtype({arg_parser_outputs['dtype'].expr})
|
|
.device({arg_parser_outputs['device'].expr})
|
|
.layout({arg_parser_outputs['layout'].expr})
|
|
.requires_grad({arg_parser_outputs['requires_grad'].expr})
|
|
.pinned_memory({arg_parser_outputs['pin_memory'].expr});
|
|
torch::utils::maybe_initialize_device(options);
|
|
"""
|
|
)
|
|
lambda_args_exprs["options"] = "options"
|
|
|
|
# 3. special case - access scattered TensorOptions fields without packing
|
|
# TODO: maybe move to the generator side as it's not related to binding.
|
|
if not has_toptions and tensor_options_args_names:
|
|
if "dtype" in tensor_options_args_names:
|
|
# we're an output-arg variant, check these args against output tensor
|
|
if not f.func.is_out_fn():
|
|
raise RuntimeError(
|
|
f"{f.func}: dtype in tensor_options_args without output arg"
|
|
)
|
|
if not all(a in tensor_options_args_names for a in ("layout", "device")):
|
|
raise RuntimeError(
|
|
f"{f.func}: incomplete tensor options for output check"
|
|
)
|
|
|
|
inits.append(
|
|
f"""\
|
|
check_out_type_matches({arg_parser_outputs['out'].expr}, {arg_parser_outputs['dtype'].expr},
|
|
{arg_parser_outputs['dtype'].is_none_expr}, {arg_parser_outputs['layout'].expr},
|
|
{arg_parser_outputs['device'].expr}, {arg_parser_outputs['device'].is_none_expr});
|
|
"""
|
|
)
|
|
# we'll set requires_grad on outgoing tensor
|
|
if "requires_grad" not in tensor_options_args_names:
|
|
raise RuntimeError(
|
|
f'{f.func}: expected "requires_grad" in tensor_options_args absent, but found [{tensor_options_args_names}]'
|
|
)
|
|
|
|
return DispatchLambdaArgumentExprs(
|
|
exprs=tuple(lambda_args_exprs[a.name] for a in lambda_args),
|
|
inits=inits,
|
|
)
|