85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
|
# mypy: ignore-errors
|
||
|
|
|
||
|
|
from inspect import getattr_static
|
||
|
|
|
||
|
|
from ..bytecode_transformation import create_call_function
|
||
|
|
from ..exc import Unsupported
|
||
|
|
from .base import VariableTracker
|
||
|
|
|
||
|
|
|
||
|
|
class SDPAParamsVariable(VariableTracker):
|
||
|
|
"""Represents the c++ params struct for scaled dot product attention.
|
||
|
|
This is a read-only container."""
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def create(tx, value, source):
|
||
|
|
from torch.backends.cuda import SDPAParams
|
||
|
|
from ..source import AttrSource
|
||
|
|
from .builder import VariableBuilder
|
||
|
|
from .torch import TorchInGraphFunctionVariable
|
||
|
|
|
||
|
|
query_var = VariableBuilder(tx, AttrSource(source, "query"))(value.query)
|
||
|
|
key_var = VariableBuilder(tx, AttrSource(source, "key"))(value.key)
|
||
|
|
value_var = VariableBuilder(tx, AttrSource(source, "value"))(value.value)
|
||
|
|
attn_mask_var = VariableBuilder(tx, AttrSource(source, "attn_mask"))(
|
||
|
|
value.attn_mask
|
||
|
|
)
|
||
|
|
dropout_var = VariableBuilder(tx, AttrSource(source, "dropout"))(value.dropout)
|
||
|
|
is_causal_var = VariableBuilder(tx, AttrSource(source, "is_causal"))(
|
||
|
|
value.is_causal
|
||
|
|
)
|
||
|
|
param_vars = [
|
||
|
|
query_var,
|
||
|
|
key_var,
|
||
|
|
value_var,
|
||
|
|
attn_mask_var,
|
||
|
|
dropout_var,
|
||
|
|
is_causal_var,
|
||
|
|
]
|
||
|
|
return TorchInGraphFunctionVariable(SDPAParams).call_function(
|
||
|
|
tx, param_vars, {}
|
||
|
|
)
|
||
|
|
|
||
|
|
def __init__(self, proxy, param_vars, **kwargs):
|
||
|
|
self.proxy = proxy
|
||
|
|
self.param_vars = param_vars
|
||
|
|
super().__init__(**kwargs)
|
||
|
|
|
||
|
|
def reconstruct(self, codegen):
|
||
|
|
assert self.source is None
|
||
|
|
assert self.param_vars is not None
|
||
|
|
codegen.load_import_from("torch._C", "_SDPAParams")
|
||
|
|
codegen.foreach(self.param_vars)
|
||
|
|
codegen.extend_output(create_call_function(len(self.param_vars), True))
|
||
|
|
|
||
|
|
def as_proxy(self):
|
||
|
|
return self.proxy
|
||
|
|
|
||
|
|
def var_getattr(self, tx, name: str) -> VariableTracker:
|
||
|
|
import torch._C
|
||
|
|
from ..source import AttrSource
|
||
|
|
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( # noqa: TRY200
|
||
|
|
f"Unsupported torch._C._SDPAParams attribute {name}"
|
||
|
|
)
|
||
|
|
|
||
|
|
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
|