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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/_support/location_config.py
location_config.LocationCaptureLevel
from enum import Enum class LocationCaptureLevel(Enum): NONE = 0 FILE_LINE_COL = 1 STACK_TRACE = 2 STACK_TRACE_WITH_SYSTEM = 3
class LocationCaptureLevel(Enum): pass
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iree-org/wave
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merger.Merger
from typing import Dict, List, Optional, Sequence from iree.compiler.ir import Attribute, InsertionPoint, Operation, StringAttr, SymbolTable class Merger: """Merges the contents of one module into another module. This performs an opinionated merge that: * Applies a heuristic to determine whether to merg...
class Merger: '''Merges the contents of one module into another module. This performs an opinionated merge that: * Applies a heuristic to determine whether to merge/rename a global or keep the existing. * Moves functions to the target, renaming on collision. * Moves initializers to th...
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iree-org/wave
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minimize_shared_allocs.LiveInterval
from dataclasses import dataclass @dataclass class LiveInterval: start: tuple[int] = (10000,) end: tuple[int] = (-1,)
@dataclass class LiveInterval: pass
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iree-org/wave
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procedural.base.AbstractIntrinsic
from wave_lang.support.ir_imports import F32Type, F64Type, IndexType, IntegerType, IrType, Location, RankedTensorType, ShapedType, Value from typing import Any, Callable, List, Optional from ..ir_utils import FunctionBuilder, ModuleBuilder class AbstractIntrinsic: """Base class for descriptor types that can be con...
class AbstractIntrinsic: '''Base class for descriptor types that can be converted to Python proxies.''' def create_intrinsic(self, value: Value) -> Intrinsic: '''Creates a proxy object that can flow through a procedural trace.''' pass def get_ir_type(self, builder: ModuleBuilder) -> IrTyp...
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iree-org/wave
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procedural.base.AbstractScalar
from ..ir_utils import FunctionBuilder, ModuleBuilder from typing import Any, Callable, List, Optional from .primitives import IrImmediateScalar, IrImmediateTensor from wave_lang.support.ir_imports import F32Type, F64Type, IndexType, IntegerType, IrType, Location, RankedTensorType, ShapedType, Value class AbstractScal...
class AbstractScalar(AbstractIntrinsic, AbstractTypedef): '''Represents a scalar value of some type.''' def __init__(self, label: str, type_producer: Callable[[], IrType]): pass def __repr__(self): pass def create_intrinsic(self, ir_value: Value) -> Intrinsic: pass def g...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/base.py
procedural.base.AbstractTensor
from wave_lang.support.ir_imports import F32Type, F64Type, IndexType, IntegerType, IrType, Location, RankedTensorType, ShapedType, Value from typing import Any, Callable, List, Optional from .primitives import IrImmediateScalar, IrImmediateTensor import torch from ..ir_utils import FunctionBuilder, ModuleBuilder class...
class AbstractTensor(AbstractIntrinsic, AbstractTypedef): '''Represents a tensor of known rank and dtype.''' def __init__(self, *size: Optional[int], dtype: torch.dtype=torch.float32): pass def __repr__(self): pass def create_intrinsic(self, ir_value: Value) -> Intrinsic: pas...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/base.py
procedural.base.AbstractTypedef
from ..ir_utils import FunctionBuilder, ModuleBuilder from wave_lang.support.ir_imports import F32Type, F64Type, IndexType, IntegerType, IrType, Location, RankedTensorType, ShapedType, Value class AbstractTypedef: """Base class for instances which declare some form of public arg/result type definition.""" def...
class AbstractTypedef: '''Base class for instances which declare some form of public arg/result type definition.''' def get_ir_type(self, builder: ModuleBuilder) -> IrType: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/base.py
procedural.base.Abstractifiable
class Abstractifiable: """Indicates that a type knows how to abstractify itself.""" def abstractify(self) -> AbstractTypedef: raise NotImplementedError
class Abstractifiable: '''Indicates that a type knows how to abstractify itself.''' def abstractify(self) -> AbstractTypedef: pass
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iree-org/wave
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procedural.base.CallableIntrinsic
class CallableIntrinsic(Intrinsic): """Intrinsic subclass that supports calls. This is separate so as to make error handling better (i.e. does not support calls) for intrinsics that are not callable. """ __slots__ = [] def __call__(self, *args, **kwargs): return current_ir_trace().hand...
class CallableIntrinsic(Intrinsic): '''Intrinsic subclass that supports calls. This is separate so as to make error handling better (i.e. does not support calls) for intrinsics that are not callable. ''' def __call__(self, *args, **kwargs): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/base.py
procedural.base.Intrinsic
from wave_lang.support.ir_imports import F32Type, F64Type, IndexType, IntegerType, IrType, Location, RankedTensorType, ShapedType, Value from collections.abc import Sequence from typing import Any, Callable, List, Optional class Intrinsic: """Objects which interact natively with the tracing system implement this."...
class Intrinsic: '''Objects which interact natively with the tracing system implement this.''' def resolve_ir_values(self, proc_trace: 'IrTrace') -> Sequence[Value]: pass def resolve_call(self, proc_trace: 'IrTrace', *args, **kwargs): pass def resolve_assignment(self, proc_trace: 'Ir...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/base.py
procedural.base.IrTrace
from ..ir_utils import FunctionBuilder, ModuleBuilder class IrTrace(FunctionBuilder): """Gets callbacks for tracing events.""" __slots__ = [] def finalize(self): """Called when the trace is finished (popped off the stack).""" def handle_call(self, target: 'Intrinsic', args, kwargs): r...
class IrTrace(FunctionBuilder): '''Gets callbacks for tracing events.''' def finalize(self): '''Called when the trace is finished (popped off the stack).''' pass def handle_call(self, target: 'Intrinsic', args, kwargs): pass def handle_assignment(self, scope, target, updated_...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/base.py
procedural.base.ProcedureTraceError
class ProcedureTraceError(Exception): def __init__(self, message: str): super().__init__(message)
class ProcedureTraceError(Exception): def __init__(self, message: str): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/base.py
procedural.base.TreeAbstractifiable
from typing import Any, Callable, List, Optional class TreeAbstractifiable: """Indicates that a type decomposes into a tree that can be abstractified.""" def abstractify_tree(self) -> Any: raise NotImplementedError
class TreeAbstractifiable: '''Indicates that a type decomposes into a tree that can be abstractified.''' def abstractify_tree(self) -> Any: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/exported_program.py
procedural.exported_program.AutoGlobalTensorDef
from ..ir_utils import GlobalAttributes, ModuleBuilder, attributes_from_argument_device_affinities, update_func_op_argument_attributes import torch from .globals import GlobalsDef, MaterializedGlobal from torch.utils._pytree import tree_flatten, tree_unflatten class AutoGlobalTensorDef(GlobalsDef): """Global defin...
class AutoGlobalTensorDef(GlobalsDef): '''Global definition that is used for arbitrary tensor literals encountered during processing. ''' def __init__(self, name: str, value: torch.Tensor, attrs: GlobalAttributes): pass def items(self): pass def schema(self): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/exported_program.py
procedural.exported_program.ExportedProgramIntrinsic
from wave_lang.support.ir_imports import FlatSymbolRefAttr, FunctionType, IrType, Operation, StringAttr, TypeAttr, Value, func_d, util_d from typing import Any, Dict, List, Optional from .tracer import IrTrace from .base import CallableIntrinsic from .primitives import IrImmediateTensor, IrTensor from torch.utils._pytr...
class ExportedProgramIntrinsic(CallableIntrinsic): def __init__(self, entry_func_op: Operation, entry_sig: torch.export.ModuleCallSignature, user_output_dtypes: List[Optional[torch.dtype]]): pass @property def function_type(self) -> FunctionType: pass @property def function_symbol(...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/exported_program.py
procedural.exported_program._Hooks
import math import torch from wave_lang.support.logging import aot_logger as logger from ..ir_utils import GlobalAttributes, ModuleBuilder, attributes_from_argument_device_affinities, update_func_op_argument_attributes from iree.compiler.extras.fx_importer import FxImporter, FxImporterHooks, GraphNodeImporter, InputInf...
class _Hooks(FxImporterHooks): def __init__(self, module_builder: ModuleBuilder): pass def store_produced_value(self, gni: GraphNodeImporter, py_value: Any, produced_ir_value: Any, info: InputInfo): pass def resolve_literal(self, gni: GraphNodeImporter, literal: Any, info: Optional[Input...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/globals.py
procedural.globals.GlobalsDef
from wave_lang.support.logging import aot_logger as logger from torch.utils._pytree import TreeSpec, tree_unflatten from ..ir_utils import GlobalAttributes, ModuleBuilder from .base import AbstractScalar, AbstractTensor, IrTrace, current_ir_trace from collections.abc import Generator, Sequence import torch from typing ...
class GlobalsDef: '''Base class for all exporting descriptors.''' def __init__(self, attrs: GlobalAttributes): pass def items(self) -> Generator[Tuple[str, Any], None, None]: '''Yields tuples of name/value exports.''' pass def schema(self) -> TreeSpec: '''A schema use...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/globals.py
procedural.globals.IrGlobalScalar
from .primitives import IrScalar, IrTensor from collections.abc import Generator, Sequence from wave_lang.support.ir_imports import IrType, Operation, Value, util_d from .base import AbstractScalar, AbstractTensor, IrTrace, current_ir_trace class IrGlobalScalar(IrScalar, MaterializedGlobal): """An IrScalar that is...
class IrGlobalScalar(IrScalar, MaterializedGlobal): '''An IrScalar that is loaded from a global and associated with its aggregate.''' def __init__(self, export_name: str, info: GlobalsDef, *, symbol_name: str, global_op: Operation, global_type: IrType): pass def resolve_ir_values(self, trace: IrT...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/globals.py
procedural.globals.IrGlobalTensor
import torch from .base import AbstractScalar, AbstractTensor, IrTrace, current_ir_trace from .primitives import IrScalar, IrTensor from wave_lang.support.ir_imports import IrType, Operation, Value, util_d from collections.abc import Generator, Sequence class IrGlobalTensor(IrTensor, MaterializedGlobal): """An IrS...
class IrGlobalTensor(IrTensor, MaterializedGlobal): '''An IrScalar that is loaded from a global and associated with its aggregate.''' def __init__(self, export_name: str, info: GlobalsDef, *, symbol_name: str, global_op: Operation, global_type: IrType, dtype: torch.dtype): pass def resolve_ir_val...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/globals.py
procedural.globals.LiveGlobalCollectionProxy
from .base import AbstractScalar, AbstractTensor, IrTrace, current_ir_trace class LiveGlobalCollectionProxy: """Proxy object around a collection which knows how to redirect setitem.""" __slots__ = ['_raw_collection'] def __init__(self, raw_collection): self._raw_collection = raw_collection de...
class LiveGlobalCollectionProxy: '''Proxy object around a collection which knows how to redirect setitem.''' def __init__(self, raw_collection): pass def __getitem__(self, key: str): pass def __setitem__(self, key, value): pass def __len__(self): pass def __...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/globals.py
procedural.globals.MaterializedGlobal
from wave_lang.support.ir_imports import IrType, Operation, Value, util_d class MaterializedGlobal: """Tags an Ir* that is duck-typed as a global.""" ir_type: IrType symbol_name: str global_op: Operation global_type: IrType
class MaterializedGlobal: '''Tags an Ir* that is duck-typed as a global.''' pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/iree_emitter.py
procedural.iree_emitter.IREEEmitter
from typing import Any, Dict, List, Optional, Tuple, Union import torch from .base import Intrinsic, ShapedTypeDynamicSizeSentinel, current_ir_trace from .primitives import IrImmediateScalar, IrImmediateTensor, IrScalar, IrTensor from wave_lang.support.conversions import TORCH_DTYPE_TO_IREE_TYPE from wave_lang.support....
class IREEEmitter: @emitter def tensor_dim(self, source: BuildableTensorType, index: int, *, dtype: Optional[torch.dtype]=None) -> 'IrScalar': '''Gets the dimension size of a tensor at a static position.''' pass @emitter def tensor_empty(self, *dims: BuildableTensorDimDecl, dtype: torc...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/primitives.py
procedural.primitives.IrImmediateScalar
from .base import Intrinsic, IrTrace, current_ir_trace from wave_lang.support.ir_imports import F32Type, IrType, RankedTensorType, Value, arith_d from collections.abc import Sequence class IrImmediateScalar(IrScalar): """Represents an IR scalar value.""" __slots__ = ['_ir_value'] def __init__(self, ir_val...
class IrImmediateScalar(IrScalar): '''Represents an IR scalar value.''' def __init__(self, ir_value: Value): pass def resolve_ir_values(self, proc_trace: IrTrace) -> Sequence[Value]: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/primitives.py
procedural.primitives.IrImmediateTensor
import torch from collections.abc import Sequence from .base import Intrinsic, IrTrace, current_ir_trace from wave_lang.support.ir_imports import F32Type, IrType, RankedTensorType, Value, arith_d class IrImmediateTensor(IrTensor): """Represents a Value in the IR under construction during procedural tracing.""" ...
class IrImmediateTensor(IrTensor): '''Represents a Value in the IR under construction during procedural tracing.''' def __init__(self, ir_value: Value, dtype: torch.dtype): pass def __repr__(self): pass def resolve_ir_values(self, proc_trace: IrTrace) -> Sequence[Value]: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/primitives.py
procedural.primitives.IrScalar
from ..ir_utils import _is_float_type, _is_integer_like_type, build_tensor_dim_value from .base import Intrinsic, IrTrace, current_ir_trace from wave_lang.support.ir_imports import F32Type, IrType, RankedTensorType, Value, arith_d class IrScalar(Intrinsic): """An intrinsic that represents a scalar value. Subc...
class IrScalar(Intrinsic): '''An intrinsic that represents a scalar value. Subclasses are responsible for providing either value or load semantics. ''' def __init__(self, ir_type: IrType): pass def set(self, other): pass def __add__(self, other): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/primitives.py
procedural.primitives.IrTensor
from collections.abc import Sequence from .base import Intrinsic, IrTrace, current_ir_trace from ..ir_utils import _is_float_type, _is_integer_like_type, build_tensor_dim_value from typing import Dict, List, Optional from wave_lang.support.ir_imports import F32Type, IrType, RankedTensorType, Value, arith_d import torch...
class IrTensor(Intrinsic): '''An intrinsic that represents a tensor value. Carries additional metadata needed to resolve dimensions and original PyTorch attributes. ''' def __init__(self, ir_type: IrType, dtype: torch.dtype): pass @property def rank(self) -> int: pass ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/support/procedural/tracer.py
procedural.tracer.ProcedureTrace
from wave_lang.support.ir_imports import DictAttr, Location, StringAttr, Value, func_d from .base import AbstractIntrinsic, Intrinsic, IrTrace, ProcedureTraceError, new_ir_trace_scope from typing import Any, Callable, List from torch.utils._pytree import tree_flatten, tree_unflatten, treespec_dumps from ..ir_utils impo...
class ProcedureTrace(IrTrace): '''Captures execution of a Python func into IR.''' def __init__(self, *, module_builder: ModuleBuilder, func_op: func_d.FuncOp, proxy_posargs, proxy_kwargs): pass @staticmethod def define_func(module_builder: ModuleBuilder, *, symbol_name: str, posargs: Sequence,...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/_support/regions.py
regions.RegionGraph
import contextlib import torch.fx as fx from .location_config import LocationCaptureConfig from typing import Dict, List, Optional, Tuple class RegionGraph: def __init__(self, *, location_capture_config: Optional[LocationCaptureConfig]=None): self.tracers: List['SubgraphTracer'] = [] self.subgraph...
class RegionGraph: def __init__(self, *, location_capture_config: Optional[LocationCaptureConfig]=None): pass @property def root_tracer(self) -> 'SubgraphTracer': pass @property def current_tracer(self) -> 'SubgraphTracer': pass def create_proxy(self, *args, **kwargs):...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/_support/regions.py
regions.SubgraphTracer
from typing import Dict, List, Optional, Tuple import torch.utils._pytree as pytree import torch.fx as fx class SubgraphTracer(fx.Tracer): def __init__(self, region_graph: RegionGraph, parent: Optional['SubgraphTracer']=None): super().__init__() self.graph = fx.Graph() self.region_graph = ...
class SubgraphTracer(fx.Tracer): def __init__(self, region_graph: RegionGraph, parent: Optional['SubgraphTracer']=None): pass def trace(self, *args, **kwargs) -> Tuple[str, List[fx.Proxy]]: pass def _create_graph_input(self, node: fx.Node, name: str, type_expr=None) -> fx.Proxy: ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/setup.py
setup.BuildCommand
import distutils.command.build class BuildCommand(distutils.command.build.build): def initialize_options(self): distutils.command.build.build.initialize_options(self) self.build_base = '_python_build'
class BuildCommand(distutils.command.build.build): def initialize_options(self): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/setup.py
setup.CMakeBuild
import sys import subprocess from pathlib import Path from setuptools.command.build_ext import build_ext import os class CMakeBuild(build_ext): def run(self): for ext in self.extensions: self.build_cmake(ext) def build_cmake(self, ext): build_dir = os.path.abspath(os.path.join(sel...
class CMakeBuild(build_ext): def run(self): pass def build_cmake(self, ext): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/setup.py
setup.CMakeExtension
from setuptools import Extension, find_namespace_packages, setup from pathlib import Path import os class CMakeExtension(Extension): def __init__(self, name: str, sourcedir: str='') -> None: super().__init__(name, sources=[]) self.sourcedir = os.fspath(Path(sourcedir).resolve())
class CMakeExtension(Extension): def __init__(self, name: str, sourcedir: str='') -> None: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/_support/shaped_type.py
shaped_type.ShapedDataType
from typing import Optional, Type, TypeVar, cast from .dtype import DataType class ShapedDataType(ShapedType): """A shaped type containing data of a specific element type. This lets us specialize with symbolic shape information. """ dtype: Optional[DataType] = None def __new__(mcls, name: str, ba...
class ShapedDataType(ShapedType): '''A shaped type containing data of a specific element type. This lets us specialize with symbolic shape information. ''' def __new__(mcls, name: str, bases, dct): pass def new_shaped_data_subtype(cls: Type[SubtypeT], *, symbolic_shape: SymbolicShapeExpr,...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/_support/shaped_type.py
shaped_type.ShapedType
from typing import Optional, Type, TypeVar, cast class ShapedType(type): """A shaped type. This lets us specialize with symbolic shape information. """ symbolic_shape: Optional[SymbolicShapeExpr] = None rank: Optional[int] def __new__(mcls, name: str, bases, dct): symbolic_shape = dct...
class ShapedType(type): '''A shaped type. This lets us specialize with symbolic shape information. ''' def __new__(mcls, name: str, bases, dct): pass def new_shaped_subtype(cls: Type[SubtypeT], *, symbolic_shape: SymbolicShapeExpr) -> Type[SubtypeT]: pass class Subtype(cl...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/lit_tests/kernel/wave/sharktank_integration.py
sharktank_integration.WaveBhsdFlashAttentionSharktankOp
from iree.compiler.ir import Context, MLIRError, Module, Operation, RankedTensorType from wave_lang.kernel.wave.utils.run_utils import set_default_run_config from wave_lang.kernel.wave.compile import WaveCompileOptions, wave_compile from wave_lang.kernel.wave.templates.attention_common import AttentionShape from wave_l...
@CustomOp.register() class WaveBhsdFlashAttentionSharktankOp(CustomOp): def select(self, ksel: KernelSelection): pass def generate(self, ksel: KernelSelection, kb: KernelBuilder): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/wave/tuner/tune_attention.py
tune_attention.AttentionConfig
from typing import Dict, List, Optional, Tuple from dataclasses import asdict, dataclass from wave_lang.kernel.wave.scheduling.schedule import SchedulingType from wave_lang.kernel.wave.constraints import MMAType @dataclass class AttentionConfig: """Configuration for attention kernel tuning.""" batch_size: int ...
@dataclass class AttentionConfig: '''Configuration for attention kernel tuning.''' pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/wave/tuner/tune_attention.py
tune_attention.TimingResult
from typing import Dict, List, Optional, Tuple from dataclasses import asdict, dataclass @dataclass class TimingResult: """Results from timing a kernel execution.""" latency_ms: float throughput_tflops: float trace_file: Optional[str] = None
@dataclass class TimingResult: '''Results from timing a kernel execution.''' pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/wave/tuner/tune_attention.py
tune_attention.TuningLogger
import numpy as np import json import torch.fx as fx from typing import Dict, List, Optional, Tuple import logging from wave_lang.kernel.wave.tuner.utils import enum_to_str, format_latency_us, latency_to_us from pathlib import Path class TuningLogger: """Custom logger for the optimization process.""" def __in...
class TuningLogger: '''Custom logger for the optimization process.''' def __init__(self, logger: logging.Logger, schedules_dir: Path): pass def set_schedule_params(self, graph: fx.Graph, initiation_interval: int, num_stages: int, resource_reservations: Optional[np.ndarray]=None, resource_names: O...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/wave/perf/utils.py
utils.BaseBenchmark
import argparse class BaseBenchmark: """Base parser class for benchmarking kernels.""" def __init__(self, description: str='Benchmarking CLI for Wave kernels', epilog: str='', formatter_class=argparse.RawTextHelpFormatter): self.parser = argparse.ArgumentParser(description=description, epilog=epilog, ...
class BaseBenchmark: '''Base parser class for benchmarking kernels.''' def __init__(self, description: str='Benchmarking CLI for Wave kernels', epilog: str='', formatter_class=argparse.RawTextHelpFormatter): pass def _add_common_args(self) -> None: pass def parse(self): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/builtins/globals.py
wave_lang.aot.builtins.globals.export_buffers
from torch.utils._pytree import TreeSpec, tree_flatten, tree_map from typing import Any, Optional from ..support.ir_utils import GlobalAttributes, NameMapCallback from torch import nn from ..support.procedural import Abstractifiable, AbstractTypedef, GlobalsDef, TreeAbstractifiable, abstractify_single_value class expo...
class export_buffers(GlobalsDef, TreeAbstractifiable): '''Exports buffers from an nn.Module. These are exposed to procedural programs as a dictionary of param/values. ''' def __init__(self, nn_module: nn.Module, *, mutable: Optional[bool]=None, external: Optional[bool]=None, external_scope: Optional[s...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/builtins/globals.py
wave_lang.aot.builtins.globals.export_global
from torch.utils._pytree import TreeSpec, tree_flatten, tree_map from typing import Any, Optional from ..support.procedural import Abstractifiable, AbstractTypedef, GlobalsDef, TreeAbstractifiable, abstractify_single_value from ..support.ir_utils import GlobalAttributes, NameMapCallback class export_global(GlobalsDef,...
class export_global(GlobalsDef, Abstractifiable): '''Exports a single global into a CompiledModule.''' def __init__(self, value: Any, *, name: str='global', mutable: Optional[bool]=None, external: Optional[bool]=None, external_scope: Optional[str]=None, name_mapper: Optional[NameMapCallback]=None, uninitializ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/builtins/globals.py
wave_lang.aot.builtins.globals.export_global_tree
from torch.utils._pytree import TreeSpec, tree_flatten, tree_map from typing import Any, Optional from ..support.ir_utils import GlobalAttributes, NameMapCallback from ..support.procedural import Abstractifiable, AbstractTypedef, GlobalsDef, TreeAbstractifiable, abstractify_single_value class export_global_tree(Global...
class export_global_tree(GlobalsDef, Abstractifiable): '''Exports a tree of globals into a CompiledModule.''' def __init__(self, tree, *, mutable: Optional[bool]=None, external: Optional[bool]=None, external_scope: Optional[str]=None, name_mapper: Optional[NameMapCallback]=None, uninitialized: Optional[bool]=...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/builtins/globals.py
wave_lang.aot.builtins.globals.export_parameters
from ..support.ir_utils import GlobalAttributes, NameMapCallback from torch import nn from typing import Any, Optional from ..support.procedural import Abstractifiable, AbstractTypedef, GlobalsDef, TreeAbstractifiable, abstractify_single_value from torch.utils._pytree import TreeSpec, tree_flatten, tree_map class expo...
class export_parameters(GlobalsDef, TreeAbstractifiable): '''Exports parameters from an nn.Module. These are exposed to procedural programs as a dictionary of param/values. ''' def __init__(self, nn_module: nn.Module, *, mutable: Optional[bool]=None, external: Optional[bool]=None, external_scope: Opti...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/builtins/jittable.py
wave_lang.aot.builtins.jittable._Hooks
from wave_lang.support.ir_imports import FlatSymbolRefAttr, FunctionType, Operation, StringAttr, SymbolTable, TypeAttr, Value, func_d, util_d from ..support.procedural import CallableIntrinsic, IrImmediateTensor, IrTensor, IrTrace, MaterializedGlobal from iree.compiler.extras.fx_importer import FxImporter, FxImporterHo...
class _Hooks(FxImporterHooks): def __init__(self, module_builder: ModuleBuilder): pass def resolve_literal(self, gni: GraphNodeImporter, literal: Any, info: Optional[InputInfo]=None) -> Optional[Value]: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/builtins/jittable.py
wave_lang.aot.builtins.jittable._Merger
from wave_lang.support.ir_imports import FlatSymbolRefAttr, FunctionType, Operation, StringAttr, SymbolTable, TypeAttr, Value, func_d, util_d from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union from ..support.ir_utils import ModuleBuilder class _Merger: __slots__ = ['context', 'from_module_op...
class _Merger: def __init__(self, to_module_builder: ModuleBuilder, from_module_op: Operation, from_symbol_table: SymbolTable, import_function_name: str): pass def merge(self) -> Optional[Operation]: pass def import_symbol_op(self, symbol_op): pass def _rename(self, from_sym...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/builtins/jittable.py
wave_lang.aot.builtins.jittable.jittable
from ..support.procedural import CallableIntrinsic, IrImmediateTensor, IrTensor, IrTrace, MaterializedGlobal from wave_lang.support.logging import aot_logger as logger from ..decompositions import current_aot_decompositions from iree.compiler.extras.fx_importer import FxImporter, FxImporterHooks, GraphNodeImporter, Inp...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/compiled_module.py
wave_lang.aot.compiled_module.CompiledModule
from pathlib import Path import sys from typing import Any, Callable, Dict, Optional, Tuple, Union from .tensor_traits import DeviceAffinity from . import builtins from .support.ir_utils import ModuleBuilder, ModuleBuilderOptions from .support.procedural.exported_program import import_exported_program from wave_lang.su...
null
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/compiled_module.py
wave_lang.aot.compiled_module.CompiledModuleClassInfo
from torch.export import ExportedProgram import logging from typing import Any, Callable, Dict, Optional, Tuple, Union from .support.procedural import GlobalsDef, ProcedureTrace, current_ir_trace from .support.ir_utils import ModuleBuilder, ModuleBuilderOptions import inspect from collections.abc import Generator from ...
class CompiledModuleClassInfo: def __init__(self, *, ir_module_name: str, options: ModuleBuilderOptions): pass def add_export(self, key: str, value: Exportable): pass @property def export_procs(self) -> Generator[Tuple[str, ExportProcDef], None, None]: pass @property d...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/compiled_module.py
wave_lang.aot.compiled_module.CompiledModuleInstanceInfo
from .support.ir_utils import ModuleBuilder, ModuleBuilderOptions from typing import Any, Callable, Dict, Optional, Tuple, Union class CompiledModuleInstanceInfo: """Info class for compiled module instances.""" __slots__ = ['class_info', 'current_import_phase', 'module_builder', 'shadow_dict'] def __init_...
class CompiledModuleInstanceInfo: '''Info class for compiled module instances.''' def __init__(self, class_info: CompiledModuleClassInfo, module_builder: ModuleBuilder): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/compiled_module.py
wave_lang.aot.compiled_module.CompiledModuleMeta
from wave_lang.support.logging import aot_logger as logger from typing import Any, Callable, Dict, Optional, Tuple, Union from .support.ir_utils import ModuleBuilder, ModuleBuilderOptions class CompiledModuleMeta(type): """Metaclass for all CompiledModule subclasses. Do not use directly. """ def __ne...
class CompiledModuleMeta(type): '''Metaclass for all CompiledModule subclasses. Do not use directly. ''' def __new__(mcls, name: str, bases, dct, *, export_name: Optional[str]=None, options: Optional[ModuleBuilderOptions]=None): pass def __getattr__(cls, key): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/compiled_module.py
wave_lang.aot.compiled_module.ExportProcDef
from .tensor_traits import DeviceAffinity from typing import Any, Callable, Dict, Optional, Tuple, Union class ExportProcDef: __slots__ = ['arg_device', 'callable', 'export_name', 'file_line_loc', 'signature'] def __init__(self, export_name: str, callable: Callable, *, signature, file_line_loc: Optional[Tuple...
class ExportProcDef: def __init__(self, export_name: str, callable: Callable, *, signature, file_line_loc: Optional[Tuple[str, int]]=None, arg_device: dict[int, DeviceAffinity] | None=None): pass def copy(self) -> 'ExportProcDef': pass def __repr__(self): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/compiled_module.py
wave_lang.aot.compiled_module.ExportTargetDef
from torch.export import ExportedProgram from .tensor_traits import DeviceAffinity from typing import Any, Callable, Dict, Optional, Tuple, Union class ExportTargetDef: def __init__(self, target: Union[Callable, ExportedProgram], *, arg_device: dict[int, DeviceAffinity] | None=None): self.target = target ...
class ExportTargetDef: def __init__(self, target: Union[Callable, ExportedProgram], *, arg_device: dict[int, DeviceAffinity] | None=None): pass def __call__(self, *args, **kwargs): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/compiled_module.py
wave_lang.aot.compiled_module.ExportedProgramDef
from .tensor_traits import DeviceAffinity from typing import Any, Callable, Dict, Optional, Tuple, Union from torch.export import ExportedProgram class ExportedProgramDef: def __init__(self, ep: ExportedProgram, *, export_name: Optional[str]=None, public: bool=False, arg_device: dict[int, DeviceAffinity] | None=N...
class ExportedProgramDef: def __init__(self, ep: ExportedProgram, *, export_name: Optional[str]=None, public: bool=False, arg_device: dict[int, DeviceAffinity] | None=None): pass def copy(self) -> 'ExportedProgramDef': pass def __repr__(self): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/compiled_module.py
wave_lang.aot.compiled_module.ImportPhase
from typing import Any, Callable, Dict, Optional, Tuple, Union import enum class ImportPhase(enum.IntEnum): TORCH_IR = 0 CUSTOM_OP_EXPANSION = 1 IMPORT = CUSTOM_OP_EXPANSION IREE_INTERNAL = 2 FULL = IREE_INTERNAL @staticmethod def parse(spec: Union[str, None, 'ImportPhase']) -> 'ImportPhas...
class ImportPhase(enum.IntEnum): @staticmethod def parse(spec: Union[str, None, 'ImportPhase']) -> 'ImportPhase': pass def __str__(self): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/compiled_module.py
wave_lang.aot.compiled_module.PyOnlyDef
class PyOnlyDef: """Exportable that does not export but can be resolved in Python.""" __slots__ = ['py_value'] def __init__(self, py_value): self.py_value = py_value def __str__(self): return str(self.py_value) def __repr__(self): return repr(self.py_value) def __call...
class PyOnlyDef: '''Exportable that does not export but can be resolved in Python.''' def __init__(self, py_value): pass def __str__(self): pass def __repr__(self): pass def __call__(self, *args, **kwargs): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/exporter.py
wave_lang.aot.exporter.ExportOutput
from pathlib import Path from typing import Any, Dict, List, Optional, Tuple, Type, Union, overload import io from .compiled_module import CompiledModule, ImportPhase, ModuleBuilderOptions from wave_lang.support.ir_imports import Context, Operation from collections.abc import Sequence from iree.compiler.api import Outp...
class ExportOutput: '''Wrapper around a CompiledModule produced by `export`.''' def __init__(self, session: Session, compiled_module: CompiledModule, *, importer_uses_session: bool=False): pass @property def mlir_module(self) -> Operation: '''Gets the MLIR module resulting from the las...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/fx_programs.py
wave_lang.aot.fx_programs.FxPrograms
from typing import TYPE_CHECKING, Any, Optional, Union import torch from pathlib import Path import os from .compiled_module import ExportTargetDef import json class FxPrograms: """Represents a named set of ExportedPrograms. This facility works around a design flaw in Torch where they conflated ExportedPr...
class FxPrograms: '''Represents a named set of ExportedPrograms. This facility works around a design flaw in Torch where they conflated ExportedPrograms as representing a single entry-point while also having each instance persist its own state_dict and constants. How many times, in how many framewo...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/fx_programs.py
wave_lang.aot.fx_programs.FxProgramsBuilder
import torch from .decompositions import current_aot_decompositions from .tensor_traits import DeviceAffinity from .compiled_module import ExportTargetDef from typing import TYPE_CHECKING, Any, Optional, Union from torch import nn import functools class FxProgramsBuilder(FxPrograms): """Builds a new set of exporte...
class FxProgramsBuilder(FxPrograms): '''Builds a new set of exported programs that are all variations of the same root nn.Module. This can be used to construct multi-entrypoint sets of ExportedPrograms in a way that alias information is preserved for lifted tensors. Usage: ``` class MyModul...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/fx_programs.py
wave_lang.aot.fx_programs.SharedStateTensor
import torch class SharedStateTensor(torch.Tensor): """A fake tensor that we shove into ExportedProgram state to share.""" @staticmethod def __new__(cls, size, dtype, shared_state_dict_key: str, is_param: bool, requires_grad=False): return torch.Tensor._make_subclass(cls, torch.empty(size, dtype=d...
class SharedStateTensor(torch.Tensor): '''A fake tensor that we shove into ExportedProgram state to share.''' @staticmethod def __new__(cls, size, dtype, shared_state_dict_key: str, is_param: bool, requires_grad=False): pass def __init__(self, size, dtype, shared_state_dict_key: str, is_param:...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/params.py
wave_lang.aot.params.ParameterArchive
from pathlib import Path from typing import List, Optional, Set, Tuple, Union from iree.runtime import ParameterIndex, ParameterIndexEntry class ParameterArchive: """Allows access to a parameter archive as CPU tensors. TODO: Add more helpers for reading tensors once we get upstream versions that have that...
class ParameterArchive: '''Allows access to a parameter archive as CPU tensors. TODO: Add more helpers for reading tensors once we get upstream versions that have that integrated. ''' def __init__(self, file_path: Optional[Union[str, Path]]=None, *, mmap: bool=True, readable: bool=True, writable: ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/params.py
wave_lang.aot.params.ParameterArchiveBuilder
from pathlib import Path from typing import List, Optional, Set, Tuple, Union from iree.runtime import ParameterIndex, ParameterIndexEntry from torch import nn import torch class ParameterArchiveBuilder: """Helper for building parameter archives from live modules.""" def __init__(self): self._index = ...
class ParameterArchiveBuilder: '''Helper for building parameter archives from live modules.''' def __init__(self): pass @property def index(self) -> ParameterIndex: pass def save(self, file_path: Union[str, Path]): '''Saves the archive.''' pass def add_tensor(...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/params.py
wave_lang.aot.params.ParameterArchiveEntry
import json import torch from iree.runtime import ParameterIndex, ParameterIndexEntry import numpy as np class ParameterArchiveEntry: """Wraps a raw ParameterIndexEntry with additional helpers.""" def __init__(self, raw: ParameterIndexEntry): self.raw = raw @property def key(self) -> str: ...
class ParameterArchiveEntry: '''Wraps a raw ParameterIndexEntry with additional helpers.''' def __init__(self, raw: ParameterIndexEntry): pass @property def key(self) -> str: pass def as_flat_tensor(self) -> torch.Tensor: '''Accesses the contents as a uint8 flat tensor. ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/tensor_traits.py
wave_lang.aot.tensor_traits.DeviceAffinity
from typing import Optional class DeviceAffinity: """This is used to provide device affinities to exported function arguments.""" def __init__(self, ordinal: int, queues: Optional[list]=None): self.ordinal = ordinal self.queues = queues def __eq__(self, other) -> bool: if not isin...
class DeviceAffinity: '''This is used to provide device affinities to exported function arguments.''' def __init__(self, ordinal: int, queues: Optional[list]=None): pass def __eq__(self, other) -> bool: pass def __repr__(self) -> str: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/tensor_traits.py
wave_lang.aot.tensor_traits.DeviceTensorTrait
from dataclasses import dataclass from typing import Optional import torch @dataclass class DeviceTensorTrait: """Represents a 'trait' that can be applied to a Tensor to signal that it is to be loaded to a speific device at execution time. """ ordinal: int queues: Optional[list] = None @static...
@dataclass class DeviceTensorTrait: '''Represents a 'trait' that can be applied to a Tensor to signal that it is to be loaded to a speific device at execution time. ''' @staticmethod def get(from_tensor: torch.Tensor) -> Optional['DeviceTensorTrait']: pass def set(self, to_tensor: torch...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/aot/tensor_traits.py
wave_lang.aot.tensor_traits.ExternalTensorTrait
from typing import Optional import torch from dataclasses import dataclass @dataclass class ExternalTensorTrait: """Represents a 'trait' that can be applied to a Tensor to signal that it is to be loaded by name from an external archive at AOT execution time. """ external_scope: str external_name: s...
@dataclass class ExternalTensorTrait: '''Represents a 'trait' that can be applied to a Tensor to signal that it is to be loaded by name from an external archive at AOT execution time. ''' @staticmethod def get(from_tensor: torch.Tensor) -> Optional['ExternalTensorTrait']: pass def set(s...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/dynamo/type_conversion.py
wave_lang.dynamo.type_conversion.NativeTypeConverter
from typing import List, Optional import re from wave_lang.support.ir_imports import Context, F64Type, IntegerType, IrType, Location, Operation, RankedTensorType, ShapedType, Value, tensor_d import functools class NativeTypeConverter: def __init__(self, context: Context): self._context = context s...
class NativeTypeConverter: def __init__(self, context: Context): pass def torch_type_to_native(self, torch_type: IrType, signless: bool=True) -> IrType: '''Converts a presumed torch type to a corresponding native type. This mirrors the type conversion in torch-mlir's BackendTypeConver...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/grid.py
wave_lang.kernel.lang.grid.Grid
from .._support.indexing import IndexExpr, IndexingContext from typing import ClassVar, Type, cast from .._support.shaped_type import ShapedType class Grid(metaclass=ShapedType): """Grid with bounding symbolic shape information in the type.""" symbolic_shape: ClassVar[tuple[IndexExpr, ...]] rank: ClassVar[...
class Grid(metaclass=ShapedType): '''Grid with bounding symbolic shape information in the type.''' def __init__(self): pass def __class_getitem__(cls, symbolic_shape: tuple[IndexExpr, ...] | IndexExpr) -> Type['Grid']: pass @property def shape(self) -> tuple[int, ...]: pas...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/kernel_buffer.py
wave_lang.kernel.lang.kernel_buffer.AddressSpace
from enum import Enum class AddressSpace(Enum): REGISTER = 0 SHARED_MEMORY = 1 GLOBAL_MEMORY = 2
class AddressSpace(Enum): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/kernel_buffer.py
wave_lang.kernel.lang.kernel_buffer.InputBuffer
class InputBuffer(KernelBuffer): usage = KernelBufferUsage.INPUT
class InputBuffer(KernelBuffer): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/kernel_buffer.py
wave_lang.kernel.lang.kernel_buffer.KernelBuffer
import torch from typing import ClassVar, Type, TypeVar, cast from .._support.indexing import IndexExpr from .._support.dtype import DataType from .. import ops class KernelBuffer(metaclass=KernelBufferMeta): """Represents a buffer in global memory. Top level kernels always operate on global memory via these ...
class KernelBuffer(metaclass=KernelBufferMeta): '''Represents a buffer in global memory. Top level kernels always operate on global memory via these buffers, and the primary operations that can be performed on them are loads/stores and DMAs to some form of compute capable local buffer. When exe...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/kernel_buffer.py
wave_lang.kernel.lang.kernel_buffer.KernelBufferMeta
from typing import ClassVar, Type, TypeVar, cast from .._support.shaped_type import ShapedDataType from .._support.dtype import DataType from .._support.indexing import IndexExpr class KernelBufferMeta(ShapedDataType): usage: KernelBufferUsage = KernelBufferUsage.NONE def new_subtype(cls: Type[SubtypeT], *, n...
class KernelBufferMeta(ShapedDataType): def new_subtype(cls: Type[SubtypeT], *, name: str | None=None, address_space: AddressSpace | None=None, symbolic_shape: tuple[IndexExpr, ...] | None=None, dtype: DataType | None=None, physical_layout: MemoryLayout | None=None, usage: KernelBufferUsage | None=None) -> Type[S...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/kernel_buffer.py
wave_lang.kernel.lang.kernel_buffer.KernelBufferUsage
from enum import Enum class KernelBufferUsage(Enum): NONE = 0 INPUT = 1 OUTPUT = 2 TEMPORARY = 3 @staticmethod def _type_name(v) -> str: if v == KernelBufferUsage.NONE: return 'KernelBuffer' elif v == KernelBufferUsage.INPUT: return 'InputBuffer' ...
class KernelBufferUsage(Enum): @staticmethod def _type_name(v) -> str: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/kernel_buffer.py
wave_lang.kernel.lang.kernel_buffer.MemoryLayout
from dataclasses import dataclass from .._support.indexing import IndexExpr @dataclass class MemoryLayout: """ Specifies the physical layout of a memory buffer in terms of its physical shape. """ shape: tuple[int | IndexExpr]
@dataclass class MemoryLayout: ''' Specifies the physical layout of a memory buffer in terms of its physical shape. ''' pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/kernel_buffer.py
wave_lang.kernel.lang.kernel_buffer.OutputBuffer
class OutputBuffer(KernelBuffer): usage = KernelBufferUsage.OUTPUT
class OutputBuffer(KernelBuffer): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/kernel_buffer.py
wave_lang.kernel.lang.kernel_buffer.TemporaryBuffer
class TemporaryBuffer(KernelBuffer): usage = KernelBufferUsage.TEMPORARY
class TemporaryBuffer(KernelBuffer): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/types.py
wave_lang.kernel.lang.types.Index
@_impl_fixed_int class Index(int): """An index type that is isomorphic to MLIR `index`. At the Python level, this is just an int. """ ...
@_impl_fixed_int class Index(int): '''An index type that is isomorphic to MLIR `index`. At the Python level, this is just an int. ''' pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/types.py
wave_lang.kernel.lang.types.Vector
class Vector: """A tensor like type that is isomorphic to MLIR `vector`. A vector has value semantics and allows computation over it. """ ...
class Vector: '''A tensor like type that is isomorphic to MLIR `vector`. A vector has value semantics and allows computation over it. ''' pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/wave_types.py
wave_lang.kernel.lang.wave_types.IndexMapping
from itertools import chain from typing import Any, ClassVar, Iterable, Optional, Sequence, Type, TypeAlias, TypeVar from .._support.indexing import IndexExpr, IndexSymbol, index_symbol from typing_extensions import Self class IndexMapping: """ Represents a mapping between 2 sets of indices. """ iters:...
class IndexMapping: ''' Represents a mapping between 2 sets of indices. ''' def __init__(self, num_iterators: int, inputs: SymbolsMap, outputs: SymbolsMap, dynamic_val_mappings: SymbolsMap | Sequence[SymbolsMap]=()) -> None: pass @property def num_iterators(self) -> int: pass ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/wave_types.py
wave_lang.kernel.lang.wave_types.Memory
from .kernel_buffer import AddressSpace, KernelBufferMeta, KernelBufferUsage, MemoryLayout from typing import Any, ClassVar, Iterable, Optional, Sequence, Type, TypeAlias, TypeVar from .._support.dtype import DataType from .._support.indexing import IndexExpr, IndexSymbol, index_symbol class Memory(metaclass=KernelBuf...
class Memory(metaclass=KernelBufferMeta): ''' Represents storage anywhere in the memory hierarchy except registers. Parameterized by a shape, address space and element type. The allocated memory is traversed by an iterator that specifies the offset, stride and size along each dimension. The sym...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/wave_types.py
wave_lang.kernel.lang.wave_types.Register
from typing import Any, ClassVar, Iterable, Optional, Sequence, Type, TypeAlias, TypeVar from .._support.indexing import IndexExpr, IndexSymbol, index_symbol from .kernel_buffer import AddressSpace, KernelBufferMeta, KernelBufferUsage, MemoryLayout from .._support.dtype import DataType class Register(metaclass=KernelB...
class Register(metaclass=KernelBufferMeta): ''' Represents virtual registers. Parameterized by a shape and element type. Instantiating this class emits a new `register` operation. ''' def __new__(cls, value: float) -> 'Register': pass def __class_getitem__(cls, shape_and_dtype: tu...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/lang/wave_types.py
wave_lang.kernel.lang.wave_types.SymbolBind
from .._support.dtype import DataType from typing import Any, ClassVar, Iterable, Optional, Sequence, Type, TypeAlias, TypeVar class SymbolBind: """ Represents a binding between a symbol and a kernel argument. """ dtype: DataType def __class_getitem__(cls, dt: DataType) -> Type['SymbolBind']: ...
class SymbolBind: ''' Represents a binding between a symbol and a kernel argument. ''' def __class_getitem__(cls, dt: DataType) -> Type['SymbolBind']: pass class Subtype(cls): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/base.py
wave_lang.kernel.ops.base.OpDispatcher
from typing import Type, TypeVar from .._support import context class OpDispatcher: """Handles dispatch of operations by their idname. Operations are dispatched by looking up a function on the dispatcher like: def handle_{idname}(self, operator, *args, **kwargs) """ __wave_context_idname__ = 'Op...
class OpDispatcher: '''Handles dispatch of operations by their idname. Operations are dispatched by looking up a function on the dispatcher like: def handle_{idname}(self, operator, *args, **kwargs) ''' @classmethod def current(cls: Type[OpDispatcherT]) -> OpDispatcherT: pass ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.Allocate
from dataclasses import dataclass, field, fields from ..lang.wave_types import IndexMapping, Memory, Register from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final import torch.fx as fx import math from ..lang.kern...
@define_op('allocate') @dataclass class Allocate(CustomOp): ''' Represents an allocation in an address space (such as shared memory). ''' @property def indexing_dims(self) -> list[IndexSymbol]: pass @property def type(self) -> 'Memory': pass @property def allocation_s...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.ApplyExpr
from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields import torch.fx as fx from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final @define_op('apply_expr') @dataclass class ApplyExpr(CustomOp): register_: fx.Proxy |...
@define_op('apply_expr') @dataclass class ApplyExpr(CustomOp): @property def type(self) -> 'Register': pass @property def indexing_dims(self) -> list[IndexSymbol]: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.AtomicOp
from ..lang.wave_types import IndexMapping, Memory, Register from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields from abc import ABC @define_op('atomic_min') @dat...
@define_op('atomic_min') @dataclass class AtomicOp(BinaryOpBase, ABC): ''' Represents an atomic operation in the graph. Takes in Register and Memory as inputs and writes the modified value back on to the buffer. Mapping attribute maps the index from wave kernel to the shared memory index the wavegro...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.BinaryOpBase
import torch.fx as fx from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields from abc import ABC from .._support.dtype import DataType, i1 @dataclass class BinaryOpB...
@dataclass class BinaryOpBase(CustomOp, ABC): ''' Represents an elementwise binary python operator. DTYPE requirement: lhs and rhs needs to have the same dtpye. Shape requirement: lhs and rhs either have same shape or their shape must be broadcastable to one...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.BinaryPyOp
from abc import ABC from dataclasses import dataclass, field, fields import operator from ..lang.wave_types import IndexMapping, Memory, Register @define_py_op(operator.add) @define_py_op(operator.sub) @define_py_op(operator.mul) @define_py_op(operator.and_) @define_py_op(operator.or_) @define_py_op(operator.truediv) ...
@define_py_op(operator.add) @define_py_op(operator.sub) @define_py_op(operator.mul) @define_py_op(operator.and_) @define_py_op(operator.or_) @define_py_op(operator.truediv) @define_interface_op('maximum') @define_interface_op('minimum') @define_interface_op('atan2') @define_interface_op('powf') @dataclass class BinaryP...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.BitcastOp
import torch.fx as fx from ..lang.wave_types import IndexMapping, Memory, Register from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields from abc import ABC from .._support.dtype import DataType, i1 @define_op('bitcast') @dataclass class BitcastOp(Custom...
@define_op('bitcast') @dataclass class BitcastOp(CustomOp, ABC): ''' Represents a bitcast operation. ''' @property def scale_factor(self): pass @property def indexing_dims(self) -> list[IndexSymbol]: pass def infer_type(self, *args): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.Broadcast
import torch.fx as fx from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final from ..lang.wave_types import IndexMapping, Memory, Register from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields from abc import ABC @define...
@define_op('broadcast') @dataclass class Broadcast(CustomOp, ABC): ''' Represents a Broadcast operation. arg: Source tensor/value to broadcast target_shape: symbolic target broadcast shape. ''' def __post_init__(self): pass @property def indexing_dims(self) -> list[IndexSymbol]:...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.CastOp
import torch.fx as fx from ..lang.wave_types import IndexMapping, Memory, Register from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields from abc import ABC from .._support.dtype import DataType, i1 @define_op('cast') @dataclass class CastOp(CustomOp, AB...
@define_op('cast') @dataclass class CastOp(CustomOp, ABC): ''' Represents a cast operation. ''' @property def indexing_dims(self) -> list[IndexSymbol]: pass def infer_type(self, *args): pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.ComparisonPyOp
import operator from ..lang.wave_types import IndexMapping, Memory, Register from dataclasses import dataclass, field, fields from abc import ABC from .._support.dtype import DataType, i1 @define_py_op(operator.eq) @define_py_op(operator.gt) @define_py_op(operator.ge) @define_py_op(operator.lt) @define_py_op(operator....
@define_py_op(operator.eq) @define_py_op(operator.gt) @define_py_op(operator.ge) @define_py_op(operator.lt) @define_py_op(operator.le) @define_py_op(operator.ne) @define_interface_op('eq') @define_interface_op('gt') @define_interface_op('ge') @define_interface_op('lt') @define_interface_op('le') @define_interface_op('n...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.Conditional
from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields import torch.fx as fx from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final @define_op('conditional') @dataclass class Conditional(NestedRegionOp): condition: f...
@define_op('conditional') @dataclass class Conditional(NestedRegionOp): @classmethod def handle(cls, graph, *args, **kwargs): pass def wrapper(f): pass @property def indexing_dims(self) -> list[IndexSymbol]: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.CustomOp
from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final import copy import numpy as np from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields from abc import ABC from .._support.regions import RegionGraph from typing_exten...
@dataclass class CustomOp(ABC): ''' Base class for all custom fx nodes. ''' @property def location(self) -> Optional[FileLineColInfo | StackTraceInfo]: pass @classmethod def from_fx_node(cls: Type[CustomOpT], node: fx.Node) -> CustomOpT: pass def __post_init__(self):...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.DebugLog
from dataclasses import dataclass, field, fields from ..lang.wave_types import IndexMapping, Memory, Register import torch.fx as fx from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final @define_op('debug_log') @dataclass class DebugLog(CustomOp): """ An op for debugging. ...
@define_op('debug_log') @dataclass class DebugLog(CustomOp): ''' An op for debugging. Represents a write to an implicit global memory location. The kernel will implicitly have an extra memory input added that will be injected by the Python kernel launcher. The memory can be accessed by passing an an...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.Extract
from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields import torch.fx as fx from ..lang.wave_types import IndexMapping, Memory, Register @define_op('extract') @dataclass class Extract(CustomOp): """ Op Rationale: Extract is an op used to rep...
@define_op('extract') @dataclass class Extract(CustomOp): ''' Op Rationale: Extract is an op used to represent extracting of a scalar from TKW's 1-D vector on the specified index. This can also be viewed as indexing/slicing on the fastest dimension. Hence, the semantic of this op is designed to ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.ExtractSlice
from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields import torch.fx as fx @define_op('extract_slice') @dataclass class ExtractSlice(CustomOp): register_: fx.Proxy offset: tuple[IndexExpr] size: tuple[IndexExpr] stride: tuple[IndexExpr] ...
@define_op('extract_slice') @dataclass class ExtractSlice(CustomOp): @property def type(self) -> 'Register': pass @property def rank(self) -> int: pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.GatherToLDS
from ..lang.wave_types import IndexMapping, Memory, Register from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields from .._support.dtype import DataType, i1 @define...
@define_op('gather_to_lds') @dataclass class GatherToLDS(CustomOp): ''' Represents an instruction that performs direct load from global to lds. Source node points to the global memory to load from and the destination node points to shared memory. ''' pass
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.GetResult
from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol import torch.fx as fx import operator from dataclasses import dataclass, field, fields from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final @define_py_op(operator.getitem) @define_op('get_result') @dataclass clas...
@define_py_op(operator.getitem) @define_op('get_result') @dataclass class GetResult(CustomOp): def infer_type(self, *args): pass @property def indexing_dims(self) -> list[IndexExpr]: pass def has_multiple_value(x): pass def is_valid_indexing_dim(x): ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.IterArg
from dataclasses import dataclass, field, fields @dataclass class IterArg(Placeholder): """ Represents a specific placeholder node in the graph that is an iter arg of a reduction node. IterArgs can be of type Register or Memory with a Shared memory address space. """ def parent_op(self): ...
@dataclass class IterArg(Placeholder): ''' Represents a specific placeholder node in the graph that is an iter arg of a reduction node. IterArgs can be of type Register or Memory with a Shared memory address space. ''' def parent_op(self): pass @property def iter_idx(self): ...
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iree-org/wave
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/iree-org_wave/wave_lang/kernel/ops/wave_ops.py
wave_lang.kernel.ops.wave_ops.Iterate
from .._support.indexing import IndexExpr, IndexSequence, IndexSymbol from dataclasses import dataclass, field, fields from .._support.regions import RegionGraph import torch.fx as fx from typing import TYPE_CHECKING, Any, Callable, Optional, Sequence, Type, TypeVar, final @define_op('iterate') @dataclass class Iterat...
@define_op('iterate') @dataclass class Iterate(NestedRegionOp): @classmethod def handle(cls, graph: RegionGraph, *args, **kwargs): pass def wrapper(f): pass @property def indexing_dims(self) -> list[IndexSymbol] | list[list[IndexSymbol]]: pass def iter_args...
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40
86
3
75
33
64
4
6
3
25