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4a7bc1d522
Provides type coverage to ~3000 LOC and 200 methods in `torch/_dynamo/variables/` This is the first part of the final step to having 100% strict type coverage in dynamo - see previous comments in https://github.com/pytorch/pytorch/pull/166535 (combined into this one PR because ghstack was giving issues...) ### Coverage report: ``` mypy torch_dynamo/variables --linecount-report /tmp/coverage_log ``` Compare before to after - we go from 3826 to 7221 lines covered Pull Request resolved: https://github.com/pytorch/pytorch/pull/166569 Approved by: https://github.com/williamwen42, https://github.com/Skylion007
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
from inspect import getattr_static
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from typing import Any, Sequence, TYPE_CHECKING, TypeGuard
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from torch._guards import Source
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from torch.backends.cuda import SDPAParams
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from torch.fx.proxy import Proxy
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from ..bytecode_transformation import create_call_function
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from ..exc import Unsupported
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from ..source import AttrSource
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from .base import VariableTracker
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if TYPE_CHECKING:
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from torch._dynamo.codegen import PyCodegen
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from torch._dynamo.symbolic_convert import InstructionTranslator
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PARAM_NAMES = [
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"query",
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"key",
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"value",
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"attn_mask",
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"dropout",
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"is_causal",
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"enable_gqa",
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]
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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(
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tx: "InstructionTranslator", value: Any, source: Source
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) -> VariableTracker:
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from .torch import TorchInGraphFunctionVariable
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params = [
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VariableTracker.build(tx, getattr(value, p), AttrSource(source, p))
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for p in PARAM_NAMES
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]
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return TorchInGraphFunctionVariable(SDPAParams).call_function(tx, params, {})
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def __init__(
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self, proxy: Proxy, param_vars: Sequence[VariableTracker], **kwargs: Any
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) -> None:
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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: "PyCodegen") -> None:
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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) -> Proxy:
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return self.proxy
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def var_getattr(self, tx: "InstructionTranslator", name: str) -> VariableTracker:
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import torch._C
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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: Any) -> TypeGuard["SDPAParams"]:
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return value is SDPAParams
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