Source code for symforce.codegen.backends.cuda.cuda_config

# ----------------------------------------------------------------------------
# SymForce - Copyright 2022, Skydio, Inc.
# This source code is under the Apache 2.0 license found in the LICENSE file.
# ----------------------------------------------------------------------------
from dataclasses import dataclass
from dataclasses import field
from pathlib import Path

import sympy
from sympy.printing.codeprinter import CodePrinter

from symforce import typing as T
from symforce.codegen.backends.cuda import cuda_code_printer
from symforce.codegen.codegen_config import CodegenConfig

CURRENT_DIR = Path(__file__).parent


[docs]@dataclass class CudaConfig(CodegenConfig): """ Code generation config for the CUDA backend. Args: doc_comment_line_prefix: Prefix applied to each line in a docstring line_length: Maximum allowed line length in docstrings; used for formatting docstrings. use_eigen_types: Use eigen_lcm types for vectors instead of lists autoformat: Run a code formatter on the generated code custom_preamble: An optional string to be prepended on the front of the rendered template cse_optimizations: Optimizations argument to pass to :func:`sf.cse <symforce.symbolic.cse>` zero_epsilon_behavior: What should codegen do if a default epsilon is not set? normalize_results: Should function outputs be explicitly projected onto the manifold before returning? override_methods: Add special function overrides in dictionary with symforce function keys (e.g. ``sympy.sin``) and a string for the new method (e.g. ``"fast_math::sin_lut"``), note that this bypasses the default namespace (so std:: won't be added in front automatically). Note that the keys here need to be sympy keys, not symengine (e.g. ``sympy.sin`` NOT ``sf.sin`` with the symengine backend). SymEngine to SymPy conversion does not work for ``Function`` types. Note that this function works in the code printer, and should only be used for replacing functions that compute the same thing but in a different way, e.g. replacing ``sin`` with ``my_lib::sin``. It should `not` be used for substituting a function with a different function, which will break derivatives and certain simplifications, e.g. you should not use this to replace ``sin`` with ``cos`` or ``sin`` with ``my_lib::cos``. extra_imports: Add extra imports to the file if you use custom overrides for some functions (e.g. add fast_math.h). Note that these are only added on a call to :meth:`generate_function <symforce.codegen.codegen.Codegen.generate_function>`, i.e. you can't define custom functions in e.g. the geo package using this scalar_type: The scalar type to use (float or double) inline: Whether to generate inline functions (in the header) or a separate ``.cu`` file containing the function definition builtin_vector_variables: Names of inputs and outputs that should use CUDA's `builtin vector types <https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#built-in-vector-types>`_ instead of pointers to scalars """ doc_comment_line_prefix: str = " * " line_length: int = 100 use_eigen_types: bool = False override_methods: T.Optional[T.Dict[sympy.Function, str]] = None extra_imports: T.Optional[T.List[str]] = None scalar_type: cuda_code_printer.ScalarType = cuda_code_printer.ScalarType.FLOAT inline: bool = True builtin_vector_variables: T.Set[str] = field(default_factory=set)
[docs] @classmethod def backend_name(cls) -> str: return "cuda"
[docs] @classmethod def template_dir(cls) -> Path: return CURRENT_DIR / "templates"
[docs] def templates_to_render(self, generated_file_name: str) -> T.List[T.Tuple[str, str]]: if self.inline: return [("function/FUNCTION.h.jinja", f"{generated_file_name}.h")] else: return [ ("function/FUNCTION.h.jinja", f"{generated_file_name}.h"), ("function/FUNCTION.cu.jinja", f"{generated_file_name}.cu"), ]
[docs] def printer(self) -> CodePrinter: kwargs: T.Mapping[str, T.Any] = {"override_methods": self.override_methods} return cuda_code_printer.CudaCodePrinter(scalar_type=self.scalar_type, **kwargs)
[docs] def format_matrix_accessor(self, key: str, i: int, j: int, *, shape: T.Tuple[int, int]) -> str: """ Format accessor for matrix types. Assumes matrices are row-major. """ CudaConfig._assert_indices_in_bounds(i, j, shape) flat_index = i * shape[1] + j if key in self.builtin_vector_variables: return f"{key}.{('x', 'y', 'z', 'w')[flat_index]}" else: return f"{key}[{flat_index}]"
[docs] @staticmethod def format_eigen_lcm_accessor(key: str, i: int) -> str: """ Format accessor for eigen_lcm types. """ raise NotImplementedError("CUDA does not support eigen_lcm")