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- # mypy: ignore-errors
- from inspect import getattr_static
- from typing import TYPE_CHECKING
- from ..bytecode_transformation import create_call_function
- from ..exc import Unsupported
- from ..source import AttrSource
- from .base import VariableTracker
- if TYPE_CHECKING:
- from torch._dynamo.codegen import PyCodegen
- from torch._dynamo.symbolic_convert import InstructionTranslator
- PARAM_NAMES = "query key value attn_mask dropout is_causal enable_gqa".split()
- class SDPAParamsVariable(VariableTracker):
- """Represents the c++ params struct for scaled dot product attention.
- This is a read-only container."""
- @staticmethod
- def create(tx: "InstructionTranslator", value, source):
- from torch.backends.cuda import SDPAParams
- from .torch import TorchInGraphFunctionVariable
- params = [
- VariableTracker.build(tx, getattr(value, p), AttrSource(source, p))
- for p in PARAM_NAMES
- ]
- return TorchInGraphFunctionVariable(SDPAParams).call_function(tx, params, {})
- def __init__(self, proxy, param_vars, **kwargs) -> None:
- self.proxy = proxy
- self.param_vars = param_vars
- super().__init__(**kwargs)
- def reconstruct(self, codegen: "PyCodegen"):
- assert self.source is None
- assert self.param_vars is not None
- codegen.add_push_null(
- lambda: codegen.load_import_from("torch._C", "_SDPAParams")
- )
- codegen.foreach(self.param_vars)
- codegen.extend_output(create_call_function(len(self.param_vars), False))
- def as_proxy(self):
- return self.proxy
- def var_getattr(self, tx: "InstructionTranslator", name: str) -> VariableTracker:
- import torch._C
- from .builder import wrap_fx_proxy
- from .misc import GetAttrVariable
- try:
- getattr_static(torch._C._SDPAParams, name)
- except AttributeError:
- # Using raise from is too verbose here
- raise Unsupported(
- f"Unsupported torch._C._SDPAParams attribute {name}"
- ) from None
- proxy = GetAttrVariable.create_getattr_proxy(self.as_proxy(), name)
- if self.source is not None:
- return wrap_fx_proxy(
- tx=tx, proxy=proxy, source=AttrSource(self.source, name)
- )
- else:
- return wrap_fx_proxy(tx=tx, proxy=proxy)
- @staticmethod
- def is_sdpa_params(value):
- from torch.backends.cuda import SDPAParams
- return value is SDPAParams
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