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https://github.com/zebrajr/pytorch.git
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79aabaf626
Significant bytecode generation API change! The new suggested convention to generating bytecode to call a function is now to wrap instructions that push a callable to the stack with `add_push_null`, then that callable is called with `create_call_function` with `push_null=False` (see diff for examples). In Python 3.13, NULL is now expected to be pushed after the callable. In <=3.12, the NULL was pushed before the callable. This change abstracts away the exact placement of the NULL, but the developer must be aware that a NULL may be needed when codegen'ing a callable. This abstraction also reduces the need for the `push_null=True` option in `create_call_function`, which removes the need to rotate a NULL to the right place on the stack with a sequence of `SWAP` instructions. Pull Request resolved: https://github.com/pytorch/pytorch/pull/129172 Approved by: https://github.com/jansel
87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
# mypy: ignore-errors
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from inspect import getattr_static
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from ..bytecode_transformation import create_call_function
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from ..exc import Unsupported
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from .base import VariableTracker
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class SDPAParamsVariable(VariableTracker):
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"""Represents the c++ params struct for scaled dot product attention.
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This is a read-only container."""
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@staticmethod
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def create(tx, value, source):
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from torch.backends.cuda import SDPAParams
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from ..source import AttrSource
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from .builder import VariableBuilder
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from .torch import TorchInGraphFunctionVariable
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query_var = VariableBuilder(tx, AttrSource(source, "query"))(value.query)
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key_var = VariableBuilder(tx, AttrSource(source, "key"))(value.key)
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value_var = VariableBuilder(tx, AttrSource(source, "value"))(value.value)
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attn_mask_var = VariableBuilder(tx, AttrSource(source, "attn_mask"))(
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value.attn_mask
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)
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dropout_var = VariableBuilder(tx, AttrSource(source, "dropout"))(value.dropout)
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is_causal_var = VariableBuilder(tx, AttrSource(source, "is_causal"))(
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value.is_causal
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)
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param_vars = [
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query_var,
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key_var,
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value_var,
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attn_mask_var,
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dropout_var,
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is_causal_var,
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]
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return TorchInGraphFunctionVariable(SDPAParams).call_function(
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tx, param_vars, {}
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)
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def __init__(self, proxy, param_vars, **kwargs):
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self.proxy = proxy
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self.param_vars = param_vars
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super().__init__(**kwargs)
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def reconstruct(self, codegen):
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assert self.source is None
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assert self.param_vars is not None
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codegen.add_push_null(
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lambda: codegen.load_import_from("torch._C", "_SDPAParams")
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)
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codegen.foreach(self.param_vars)
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codegen.extend_output(create_call_function(len(self.param_vars), False))
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def as_proxy(self):
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return self.proxy
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def var_getattr(self, tx, name: str) -> VariableTracker:
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import torch._C
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from ..source import AttrSource
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from .builder import wrap_fx_proxy
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from .misc import GetAttrVariable
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try:
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getattr_static(torch._C._SDPAParams, name)
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except AttributeError:
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# Using raise from is too verbose here
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raise Unsupported(
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f"Unsupported torch._C._SDPAParams attribute {name}"
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) from None
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proxy = GetAttrVariable.create_getattr_proxy(self.as_proxy(), name)
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if self.source is not None:
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return wrap_fx_proxy(
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tx=tx, proxy=proxy, source=AttrSource(self.source, name)
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)
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else:
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return wrap_fx_proxy(tx=tx, proxy=proxy)
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@staticmethod
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def is_sdpa_params(value):
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from torch.backends.cuda import SDPAParams
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return value is SDPAParams
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