from __future__ import annotations import contextlib import dataclasses import functools import itertools import logging import textwrap import traceback import typing from collections.abc import Generator, Iterable, Sequence from contextlib import AbstractContextManager, nullcontext from enum import Enum from functools import partial from typing import ( Any, Callable, ClassVar, Literal, Optional, overload, TYPE_CHECKING, TypeVar, Union, ) from typing_extensions import assert_never, Never, TypeAlias from unittest.mock import patch import sympy from sympy import Expr, Integer, Symbol import torch._export.serde.schema as export_schema import torch._library.utils as library_utils import torch._logging import torch.fx import torch.utils._pytree as pytree from torch._dynamo.utils import identity from torch._export.serde.serialize import GraphModuleSerializer from torch._higher_order_ops.auto_functionalize import can_auto_functionalize from torch._inductor import metrics from torch._prims_common import ( compute_required_storage_length, is_boolean_dtype, is_float_dtype, make_channels_last_strides_for, StrideType, ) from torch._subclasses.fake_tensor import get_schema_info from torch.fx.experimental.symbolic_shapes import ( compute_unbacked_bindings, free_unbacked_symbols, rebind_unbacked, resolve_unbacked_bindings, ShapeEnv, SymTypes, ) from torch.utils._ordered_set import OrderedSet from torch.utils._sympy.functions import CleanDiv, FloorDiv, ModularIndexing from torch.utils._sympy.symbol import SymT from . import config, dependencies from .codegen.common import ( BackendFeature, get_scheduling_for_device, index_prevent_reordering, ) from .dependencies import ( Dep, extract_free_unbacked_symbols, extract_input_node_reduction_ranges, extract_read_writes, var_builder, ) from .loop_body import LoopBody from .ops_handler import OpCounterCSE, OpCountResult, ReductionType, StoreMode from .runtime.benchmarking import benchmarker from .runtime.hints import DeviceProperties, ReductionHint from .utils import ( argsort, argsort_sym, cache_on_self, ceildiv, convert_shape_to_inductor, convert_shape_to_symint, developer_warning, get_kernel_metadata, ir_dataclass, is_dynamic, is_gpu, sympy_dot, sympy_index_symbol, sympy_index_symbol_with_prefix, sympy_product, sympy_subs, ) from .virtualized import ops, OpsValue, V if TYPE_CHECKING: from torch.fx.node import Node from .codegen.cuda.cuda_template import CUDATemplate from .graph import GraphLowering from .utils import IndentedBuffer else: CUDATemplate: TypeAlias = object try: import triton triton_version = triton.__version__ has_triton = True except ImportError: triton_version = None has_triton = False _T = TypeVar("_T") _U = TypeVar("_U") _V = TypeVar("_V") _IntLike: TypeAlias = Union[int, Expr] _NumLike: TypeAlias = Union[int, float, Expr] log = logging.getLogger(__name__) indent = functools.partial(textwrap.indent, prefix=" ") aten = torch.ops.aten """ [Note: Inductor IR] Inductor's IR is produced by executing 'lowering' code (see lowering.py). Each lowering is registered to a particular aten operator, and expects inputs that correspond to the aten schema. However, in place of torch Tensor inputs, lowerings expect Inductor TensorBox inputs. TensorBox IR represents torch tensors. Tensors are sometimes single objects owning storage, and sometimes views of another Tensor's storage. Mutating tensor operations (such as add_()) affect the underlying storage and any associated views. Other operations (such as .t_()) update metadata about the current view but don't modify the underlying storage. To model this in Inductor, the IR distinguishes between TensorBox, View, StorageBox and Buffer. TensorBox is the top level IR construct that any lowering should produce and maps to a torch.Tensor output from an operation. But just as torch.Tensors take different forms, TensorBox IR can reference View IR or directly reference StorageBox IRs. Some Inductor lowerings produce new sets of 'Box'es, while others (such as .t() or other view ops) may take an existing TensorBox and point it to a new underlying View IR. Tensors that directly own storage are represented as a chain of: TensorBox -> StorageBox -> Buffer where Buffer is a simple (1D) allocation, and StorageBox introduces the concept of a Layout. If you mutate the data of such a tensor, we swing the StorageBox pointer to point to a new buffer (leaving the old buffer unmodified and functionalizing the operation). Tensors backed by views add one more indirection to the IR. TensorBox -> View -> StorageBox -> Buffer In these cases, the underlying StorageBox/Buffer will be shared with the pre-view TensorBox. Computation is represented by Operation nodes, with each operation producing 1 or more output Buffers. In the case of mutations, these will be new Buffers that have the mutated buffer listed in its get_mutation_names(). It is also possible to have an InputBuffer for which there is no corresponding Operation, e.g. it may be a graph input or compile time constant. """ _NodeOrNodes: TypeAlias = Union[ int, "TensorBox", dict[str, "TensorBox"], "Symbol", "IRNode", Sequence[ Optional[Union[int, dict[str, "TensorBox"], "TensorBox", "Symbol", "IRNode"]] ], ] @dataclasses.dataclass(frozen=True) class GraphPartitionSignature: # mapping from partition input name to IRNode or Expr. Need the name str since # we cannot get name from Expr. input_nodes: dict[str, Union[IRNode, sympy.Expr, TorchBindObject]] output_nodes: list[IRNode] # mapping from partition input name to a boolean for whether deallocating it # in the partition function input_deallocation: dict[str, bool] skip_cudagraph: bool def validate_ir(node_or_nodes: Optional[_NodeOrNodes]) -> None: def _check_tensorbox(nodes: Optional[_NodeOrNodes]) -> None: # Could expand this to check deeper properties # (e.g. TensorBox points to View or StorageBox) if nodes is None: pass elif isinstance(nodes, (list, tuple)): for node in nodes: _check_tensorbox(node) elif isinstance(nodes, dict): for node in nodes.values(): _check_tensorbox(node) else: assert isinstance( nodes, ( ExpandView, DynamicScalar, AssertScalar, TensorBox, sympy.logic.boolalg.Boolean, Expr, int, EffectfulKernel, ShapeAsConstantBuffer, ), ), ( f"Found {type(nodes)}, which is not a supported top level IR node. See [Note: Inductor IR]" ) # Be picky about the accepted data structure (don't use pytree here) _check_tensorbox(node_or_nodes) def ops_wrapper(name: str) -> Callable[..., OpsValue]: assert isinstance(name, str) def fn(*args: object, **kwargs: object) -> OpsValue: return getattr(ops, name)(*args, **kwargs) return fn def inverse_reorder(order: Sequence[int]) -> Callable[[Sequence[_T]], Sequence[_T]]: inv_order = dict(zip(order, range(len(order)))) def reindex(index: Sequence[_T]) -> Sequence[_T]: assert len(index) == len(inv_order) return [index[inv_order[i]] for i in range(len(index))] return reindex def same_reorder(order: Sequence[int]) -> Callable[[Sequence[_T]], Sequence[_T]]: def reindex(index: Sequence[_T]) -> Sequence[_T]: assert len(index) == len(order) return [index[order[i]] for i in range(len(index))] return reindex def fuse_reindexing( reindex1: Callable[[Sequence[_U]], Sequence[_V]], reindex2: Callable[[Sequence[_T]], Sequence[_U]], ) -> Callable[[Sequence[_T]], Sequence[_V]]: def reindex(index: Sequence[_T]) -> Sequence[_V]: return reindex1(reindex2(index)) return reindex NHWC_STRIDE_ORDER = [3, 0, 2, 1] NHWDC_STRIDE_ORDER = [4, 0, 3, 2, 1] def get_fill_order( seq: Sequence[Union[int, torch.SymInt, Expr]], shape_env: Optional[ShapeEnv] = None ) -> Sequence[int]: """ Convert strides to fill order (argsort) """ if shape_env is None: sorted_idx: Sequence[int] = argsort(seq) else: # argsort_sym handles unbacked symints (with the help of the shape_env) sorted_idx = argsort_sym(shape_env, seq) return sorted_idx def stride_order2fill_order(order: Sequence[Union[int, Integer]]) -> Sequence[int]: """ Convert stride order to fill order For channel last format, stride order = [3, 0, 2, 1] and fill order = [1, 3, 2, 0] """ lookup = {pos: idx for idx, pos in enumerate(order)} fill_order = [lookup[i] for i in range(len(order))] return fill_order def get_stride_order( seq: Sequence[Union[int, torch.SymInt, Expr]], shape_env: Optional[ShapeEnv] = None ) -> Sequence[int]: """ Convert strides to stride order """ sorted_idx: Sequence[int] = get_fill_order(seq, shape_env) out = [0 for _ in range(len(seq))] for i, elem in enumerate(sorted_idx): out[elem] = i return out @overload def ir_node_to_tensor(x: Literal[None], guard_shape: bool = True) -> None: ... @overload def ir_node_to_tensor(x: IRNode, guard_shape: bool = True) -> torch.Tensor: ... def ir_node_to_tensor( x: Optional[IRNode], guard_shape: bool = True ) -> Optional[torch.Tensor]: if x is None: return None shape_fn: Callable[[Union[int, Expr]], Union[int, Expr]] if not guard_shape: shape_fn = V.graph.sizevars.size_hint else: shape_fn = identity size = [shape_fn(s) for s in x.get_size()] stride: StrideType if is_storage_and_layout(x): stride = [shape_fn(s) for s in x.get_layout().stride] else: stride = FlexibleLayout.contiguous_strides(size) dtype = x.get_dtype() device = x.get_device() size = convert_shape_to_symint(size) stride = convert_shape_to_symint(stride) with V.graph.sizevars.shape_env.suppress_guards(): t = torch.empty_strided( size=size, stride=stride, dtype=dtype, device=device ).zero_() return t def may_convert_to_optional( value: Optional[Sequence[_T]], ) -> Optional[Sequence[Optional[_T]]]: if isinstance(value, list) and not value: # [None] makes sure the cpp wrapper codegen will generate something like # {std::nullopt} instead of {} return [None] return value def get_device_type( x: Union[IRNode, OutputSpec, torch.device, None, str], ) -> Optional[str]: if isinstance(x, str) or x is None: return x elif isinstance(x, torch.device): return x.type elif isinstance(x, (IRNode, OutputSpec)): return get_device_type(x.get_device()) assert_never(f"get_device_type({x}: {type(x).__name__})") def is_triton(x: Union[IRNode, torch.device, None, str]) -> bool: device = get_device_type(x) # Special case cpu and cuda as using the method below # to determine if the scheduler is a triton scheduler subclass # requires instantiating a scheduler for them if device in ["cpu", "cuda"]: if getattr(config, f"{device}_backend") == "triton": return True return False if ( device is None or (device_scheduling := get_scheduling_for_device(device)) is None ): return False from .codegen.triton import TritonScheduling assert isinstance(device_scheduling, type) return issubclass(device_scheduling, TritonScheduling) def is_cpu(x: Union[IRNode, torch.device, None, str]) -> bool: return get_device_type(x) == "cpu" def is_aligned_realized_tensor(x: Union[Buffer, TensorBox], alignment: int) -> bool: if not isinstance(x, IRNode) or x.maybe_get_stride() is None: return False aligned_strides = all( (V.graph.sizevars.size_hint(x.get_stride()[i]) % alignment) == 0 for i in range(len(x.get_stride()) - 1) ) # if the last dim size is <= 1, stride doesnt matter aligned_last_dim = ( V.graph.sizevars.size_hint(x.get_stride()[-1]) == 1 or V.graph.sizevars.size_hint(x.get_size()[-1]) <= 1 ) return aligned_last_dim and aligned_strides def significant_strides_equal( strides1: Sequence[_IntLike], strides2: Sequence[_IntLike], shape: Sequence[_IntLike], ) -> bool: """ Returns true if the strides are equal, ignoring dimensions of size 1 . """ assert len(shape) == len(strides1) and len(strides1) == len(strides2) for dim, s1, s2 in zip(shape, strides1, strides2): if V.graph.sizevars.statically_known_leq(dim, 1): # type: ignore[arg-type] continue if not V.graph.sizevars.statically_known_equals( s1, s2 ) and not V.graph.sizevars.symbolic_hint(s1) == V.graph.sizevars.symbolic_hint( s2 ): return False return True def try_match_insignificant_strides( tensor: Union[TensorBox, BaseView], strides: Sequence[Union[int, torch.SymInt]], ) -> Union[TensorBox, BaseView]: """ Tries to match the strides of the tensor to those in the meta_strides. Strides of insignificant dimensions - size 0 or 1 - will be updated. If there are real stride differences (NHWC vs NCHW), or the tensor is not realized, then the input will be returned """ if not is_storage_and_layout(tensor): return tensor if all( V.graph.sizevars.statically_known_equals(s1, s2) for s1, s2 in zip(strides, tensor.get_stride()) ): return tensor # type: ignore[arg-type] if not significant_strides_equal(strides, tensor.get_stride(), tensor.get_size()): return tensor storage, old_layout = as_storage_and_layout(tensor) new_stride = [*old_layout.stride] for i, s in enumerate(tensor.get_size()): if V.graph.sizevars.statically_known_leq(s, 1): # type: ignore[arg-type] new_stride[i] = strides[i] new_layout = FixedLayout( old_layout.device, old_layout.dtype, old_layout.size, new_stride, old_layout.offset, ) return TensorBox(ReinterpretView(data=storage, layout=new_layout)) class IRNode: _current_origins: ClassVar[OrderedSet[Any]] = OrderedSet() # NB: These are kinda weird, origins: OrderedSet[Any] = dataclasses.field(init=False) traceback: Optional[list[str]] = dataclasses.field(init=False) origin_node: Optional[torch.fx.Node] = dataclasses.field(init=False) @staticmethod @contextlib.contextmanager def current_origins(origins: OrderedSet[Node]) -> Generator[None, None, None]: old = IRNode._current_origins IRNode._current_origins = old | origins try: yield finally: IRNode._current_origins = old def _post_init_setattr(self, attr: str, value: Any) -> None: # Intended for use in __post_init__ for enforcing an invariant on a dataclass # If you must, can also be used for setting provenance info # We would like to try and minimize these usages though object.__setattr__(self, attr, value) def __post_init__(self) -> None: self._post_init_setattr("origins", OrderedSet(self._current_origins)) self._post_init_setattr( "traceback", traceback.format_stack() if config.debug_ir_traceback else None ) self._post_init_setattr("origin_node", None) def get_read_names(self) -> OrderedSet[str]: return OrderedSet(dep.name for dep in self.get_reads()) def get_traceback(self) -> Optional[list[str]]: return self.traceback def get_origin_node(self) -> Optional[torch.fx.Node]: return self.origin_node def get_defining_op(self) -> Optional[Operation]: return None def common_repr(self, shorten: bool = True) -> Sequence[str]: origins = f"origins={getattr(self, 'origins', '')}" if shorten and len(origins) > 64: # this can get *very* long origins = f"{origins[:61]}..." return [origins] def str_helper( self, lines: Sequence[object], shorten: bool = True, multiline: bool = True ) -> str: lines = list(lines) + list(self.common_repr(shorten)) lines = list(map(str, lines)) if multiline: new_lines = indent(",\n".join(lines)) return f"{type(self).__name__}(\n{new_lines}\n)" else: return f"{type(self).__name__}({lines})" def get_dtype(self) -> torch.dtype: return self.dtype def maybe_get_dtype(self) -> Optional[torch.dtype]: try: return self.get_dtype() except NotImplementedError: return None def get_layout(self) -> Layout: raise NotImplementedError(f"get_layout() is not implemented by {type(self)}!") def maybe_get_layout(self) -> Optional[Layout]: try: return self.get_layout() except NotImplementedError: return None def get_output_spec(self) -> OutputSpec: return self.get_layout() def maybe_get_output_spec(self) -> Optional[OutputSpec]: try: return self.get_output_spec() except NotImplementedError: return None def has_tensor_output(self) -> bool: """True for single tensor output (excludes MultiOutput)""" return isinstance(self.maybe_get_output_spec(), Layout) def get_size(self) -> Sequence[Expr]: raise NotImplementedError(f"get_size() is not implemented by {type(self)}!") def maybe_get_size(self) -> Optional[Sequence[_IntLike]]: try: return self.get_size() except NotImplementedError: return None @property def shape(self) -> Union[_IntLike, sympy.Rel, Sequence[_IntLike]]: return self.get_size() def get_numel(self) -> Expr: return sympy_product(self.get_size()) def is_zero_elements(self) -> bool: return V.graph.sizevars.is_expr_static_and_true(sympy.Eq(self.get_numel(), 0)) def realize(self) -> Optional[str]: """ If the IRNode refers to data which has not been materialized (e.g., it is a Pointwise/Reduction that could potentially have more compute fused into it), realize the IRNode into physical memory, ending the possibility of fusing into it, but allowing, e.g., multiple users to access the data without having to recompute. Check StorageBox.realize for a particularly notable implementation. TODO(ezyang): I think, in principle, every IRNode should have an implementation of this, and most of the time no-op is OK, but you really do have to audit each IRNode for this, so for now, raise an error if it's not implemented. Note that some code in graph.py will catch this thrown error and suppress it with a warning. """ raise NotImplementedError(f"realize NYI on {type(self)}") def codegen_reference(self, writer: Optional[IndentedBuffer] = None) -> str: raise NotImplementedError(f"codegen_reference NYI on {type(self)}") def get_device(self) -> Optional[torch.device]: return None def get_device_or_error(self) -> torch.device: device = self.get_device() assert device is not None return device def has_exceeded_max_reads(self) -> bool: return False def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: raise NotImplementedError(type(self).__name__) def make_indexer(self) -> Callable[[Sequence[Expr]], Expr]: raise NotImplementedError(type(self).__name__) def get_stride(self) -> Sequence[_IntLike]: raise NotImplementedError(type(self).__name__) def maybe_get_stride(self) -> Optional[Sequence[_IntLike]]: try: return self.get_stride() except NotImplementedError: return None def get_name(self) -> str: raise NotImplementedError(type(self).__name__) def maybe_get_name(self) -> Optional[str]: try: return self.get_name() except NotImplementedError: return None def has_large_inner_fn(self, threshold: Optional[int] = None) -> bool: return False def mark_reuse(self, users: int) -> None: pass def realize_hint(self) -> None: pass def unwrap_view(self) -> IRNode: raise NotImplementedError(type(self).__name__) def freeze_layout(self) -> None: raise NotImplementedError(type(self).__name__) def freeze_layout_with_stride_order( self, order: list[int], allow_padding: bool = False ) -> None: raise NotImplementedError(type(self).__name__) def freeze_layout_with_fill_order(self, order: list[int]) -> None: raise NotImplementedError(type(self).__name__) def freeze_layout_with_same_order(self, stride: list[_IntLike]) -> None: raise NotImplementedError(type(self).__name__) def freeze_layout_with_exact_strides( self, exact_strides: list[_IntLike], allow_padding: bool = False ) -> None: raise NotImplementedError(type(self).__name__) def get_read_writes(self) -> dependencies.ReadWrites: raise NotImplementedError(type(self).__name__) def get_reads(self) -> OrderedSet[Dep]: return self.get_read_writes().reads def num_reads(self) -> int: return len(self.get_reads()) def get_storage_numel(self) -> _IntLike: raise NotImplementedError(type(self).__name__) def get_unbacked_symbol_uses(self) -> OrderedSet[Symbol]: raise NotImplementedError(type(self).__name__) def get_reduction_type(self) -> Optional[str]: raise NotImplementedError(type(self).__name__) def get_reduction_size(self) -> Sequence[sympy.Expr]: raise NotImplementedError(type(self).__name__) def is_extern(self) -> bool: return False def is_no_op(self) -> bool: return False def constant_to_device(self, device: torch.device) -> IRNode: raise NotImplementedError(type(self).__name__) def get_mutation_names(self) -> Sequence[str]: raise NotImplementedError(type(self).__name__) def get_operation_name(self) -> str: raise NotImplementedError(type(self).__name__) def get_inputs_that_alias_output(self) -> Sequence[str]: raise NotImplementedError(type(self).__name__) if TYPE_CHECKING: @property def dtype(self) -> torch.dtype: ... @ir_dataclass(frozen=False) class Operation: def __post_init__(self) -> None: self.operation_name: Optional[str] = None def get_device(self) -> Optional[torch.device]: raise NotImplementedError def get_origin_node(self) -> Optional[torch.fx.Node]: assert hasattr(self, "origin_node") return self.origin_node def get_origins(self) -> OrderedSet[Any]: assert hasattr(self, "origins") return self.origins def get_operation_name(self) -> str: assert self.operation_name is not None return self.operation_name def is_extern(self) -> bool: return False def is_no_op(self) -> bool: return False def get_read_writes(self) -> dependencies.ReadWrites: raise NotImplementedError def is_user_of(self, name: str) -> bool: return name in self.get_read_names() def get_read_names(self) -> OrderedSet[str]: return OrderedSet(dep.name for dep in self.get_reads()) def get_reads(self) -> OrderedSet[Dep]: return self.get_read_writes().reads def get_outputs(self) -> list[Buffer]: raise NotImplementedError def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: """ Returns the unbacked symbols which are required to be in scope in order to successfully perform codegen for this buffer. For example, a buffer that corresponds to an extern kernel call that takes i0 as an argument would return {i0} here. This is used to generate necessary dependencies that ensure we actually bind i0 in codegen before you try to use it. Note that this is NOT transitive; in particular, if this buffer takes in as input another buffer with dynamic shape (e.g., (i0,)), we will not report it here, because you will already have a dependency on that buffer, which will eventually have a dependency on i0 if necessary. """ return OrderedSet() def get_workspace_size(self) -> int: """ Gets extra global memory size needed by this buffer. Some algorithms (e.g. group gemm) may require extra global memory in the generated code. """ return 0 @ir_dataclass class Loops(IRNode): device: torch.device dtype: torch.dtype inner_fn: Callable[..., Any] ranges: Sequence[_IntLike] def get_unbacked_symbol_uses(self) -> OrderedSet[Symbol]: return OrderedSet().union( *(free_unbacked_symbols(e) for e in self.ranges), self.inner_fn_free_unbacked_symbols(), ) def _to_str(self, names: Sequence[str]) -> str: return self.str_helper( [ f"'{self.device.type}'", str(self.dtype), self.inner_fn_str(), ] + [f"{name}={getattr(self, name)}" for name in names] + [f"origin_node={self.origin_node!r}"] ) def __post_init__(self) -> None: super().__post_init__() def __str__(self) -> str: return self._to_str(("ranges",)) __repr__ = __str__ def get_device(self) -> Optional[torch.device]: return self.device def get_origin_node(self) -> Optional[torch.fx.Node]: return self.origin_node def get_size(self) -> Sequence[Expr]: return self.ranges def get_pointwise_size(self) -> Sequence[Expr]: return self.ranges @classmethod def create(cls, *args: Any, **kwargs: Any) -> TensorBox: origin_node = kwargs.pop("origin_node", None) tb = kwargs.pop("traceback", None) # if "origin_node" in kwargs: # breakpoint() r = cls(*args, **kwargs) # Need to explicitly set origin_node here to propagate it down. # todo(chilli): I think it would be better for IRNode to directly set # origin_node r._post_init_setattr("origin_node", origin_node) r._post_init_setattr("traceback", tb or r.traceback) return TensorBox.create(r) @staticmethod def _index(ranges: Sequence[_IntLike], prefix: SymT = SymT.INDEX) -> Sequence[Expr]: return [ sympy.S.Zero if s == 1 else sympy_index_symbol_with_prefix(prefix, n) for n, s in enumerate(ranges) ] @cache_on_self def inner_fn_opcount(self) -> OpCountResult: opcounter = OpCounterCSE(V.MockHandler()) with ( V.set_ops_handler(opcounter), patch.object(FlexibleLayout, "allow_indexing", True), ): self.inner_fn(*self.inner_fn_args()) return opcounter.getvalue() def inner_fn_args(self) -> Sequence[Sequence[_IntLike]]: return (self._index(self.ranges),) @cache_on_self def inner_fn_str(self) -> str: return V.KernelFormatterHandler.ir_to_string( self.inner_fn, *self.inner_fn_args() ) def has_large_inner_fn(self, threshold: Optional[int] = None) -> bool: if threshold is None: threshold = 0 threshold = max(threshold, config.realize_opcount_threshold) return self.inner_fn_opcount().num_ops > threshold def inner_fn_free_unbacked_symbols(self) -> OrderedSet[Symbol]: index = self._index(self.ranges) return extract_free_unbacked_symbols(self.inner_fn, index) def get_reads(self) -> OrderedSet[Dep]: with patch.object(FlexibleLayout, "allow_indexing", True): if self.get_reduction_type(): return extract_read_writes( self.make_loader(), self.get_size(), self.get_reduction_size(), ).reads else: return extract_read_writes( self.make_loader(), self.get_size(), ).reads def get_read_names(self) -> OrderedSet[str]: return OrderedSet(self.inner_fn_opcount().read_buffers) def num_reads(self) -> int: return len(self.inner_fn_opcount().read_buffers) def get_reduction_size(self) -> Sequence[sympy.Expr]: raise NotImplementedError( f"get_reduction_size() is not implemented by {type(self)}!" ) def get_reduction_type(self) -> Optional[str]: raise NotImplementedError( f"get_reduction_type() is not implemented by {type(self)}!" ) def constant_to_device(self, device: torch.device) -> IRNode: raise NotImplementedError( f"constant_to_device() is not implemented by {type(self)}!" ) def nop_loader_fn(idx: Union[Expr, Sequence[Expr]], *, dtype: torch.dtype) -> OpsValue: if dtype.is_floating_point: return ops.constant(float("nan"), dtype) else: return ops.constant(0, dtype) @ir_dataclass class Pointwise(Loops): def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: # Make zero-element loops into a no-op if self.is_zero_elements(): return partial(nop_loader_fn, dtype=self.dtype) return self.inner_fn def get_reduction_size(self) -> Sequence[sympy.Expr]: return [] def get_reduction_type(self) -> Optional[str]: return None def store_output( self, output_name: Optional[str], indexer: Callable[[Sequence[Expr]], Never], vars: Sequence[Expr], ) -> None: loader = self.make_loader() return ops.store(output_name or "unnamed", indexer(vars), loader(vars)) def constant_to_device(self, device: torch.device) -> IRNode: """Move this to a given device. Requires that all reads are to constants.""" loader = self.make_loader() loader = patch.object(ConstantBuffer, "override_device", device)(loader) return Pointwise( device=device, dtype=self.dtype, inner_fn=loader, ranges=self.ranges ) @ir_dataclass class Scatter(Pointwise): output_indexer: Callable[[Sequence[Expr]], Expr] scatter_mode: StoreMode = None def constant_to_device(self, device: torch.device) -> IRNode: """Move this to a given device. Requires that all reads are to constants.""" loader = self.make_loader() loader = patch.object(ConstantBuffer, "override_device", device)(loader) return Scatter( device=device, dtype=self.dtype, inner_fn=loader, ranges=self.ranges, output_indexer=self.output_indexer, scatter_mode=self.scatter_mode, ) def store_output( self, output_name: Optional[str], indexer: Callable[[Sequence[Expr]], Never], vars: Sequence[Expr], ) -> None: loader = self.make_loader() if output_name is None: output_name = "unnamed" return ops.store( output_name, indexer(self.output_indexer(vars)), loader(vars), mode=self.scatter_mode, ) REDUCTION_COMBINE_FN: dict[str, Callable[..., OpsValue]] = { "any": ops_wrapper("logical_or"), "max": ops_wrapper("maximum"), "min": ops_wrapper("minimum"), "prod": ops_wrapper("mul"), "sum": ops_wrapper("add"), "xor_sum": ops_wrapper("bitwise_xor"), } def get_reduction_combine_fn( reduction_type: str, dtype: torch.dtype, arg_break_ties_left: bool = True ) -> Callable[..., object]: if reduction_type in REDUCTION_COMBINE_FN: return REDUCTION_COMBINE_FN[reduction_type] elif reduction_type in ("argmax", "argmin"): def argmax_combine_fn( a: tuple[object, object], b: tuple[object, object] ) -> tuple[OpsValue, OpsValue]: a_value, a_index = a b_value, b_index = b if reduction_type == "argmin": mask = ops.lt(a_value, b_value) else: mask = ops.gt(a_value, b_value) equal = ops.eq(a_value, b_value) if is_float_dtype(dtype): a_isnan = ops.ne(a_value, a_value) b_isnan = ops.ne(b_value, b_value) mask = ops.logical_or(mask, ops.gt(a_isnan, b_isnan)) equal = ops.logical_or(equal, ops.logical_and(a_isnan, b_isnan)) tie = ( ops.lt(a_index, b_index) if arg_break_ties_left else ops.gt(a_index, b_index) ) mask = ops.logical_or(mask, ops.logical_and(equal, tie)) return ( ops.where(mask, a_value, b_value), ops.where(mask, a_index, b_index), ) return argmax_combine_fn elif reduction_type == "welford_combine": def welford_combine_fn( a: tuple[OpsValue, OpsValue, OpsValue], b: tuple[OpsValue, OpsValue, OpsValue], ) -> tuple[OpsValue, OpsValue, OpsValue]: a_mean, a_m2, a_weight = a b_mean, b_m2, b_weight = b delta = b_mean - a_mean new_weight = a_weight + b_weight w2_over_w = b_weight / new_weight return ( a_mean + delta * w2_over_w, a_m2 + b_m2 + delta * delta * a_weight * w2_over_w, new_weight, ) return welford_combine_fn else: raise NotImplementedError(f"unknown reduction_type={reduction_type}") @ir_dataclass class Reduction(Loops): reduction_ranges: Sequence[_IntLike] reduction_type: ReductionType # self.dtype represents the dst dtype src_dtype: torch.dtype reduction_hint: ReductionHint def __str__(self) -> str: return self._to_str(("ranges", "reduction_ranges", "reduction_type")) __repr__ = __str__ def get_unbacked_symbol_uses(self) -> OrderedSet[Symbol]: return super().get_unbacked_symbol_uses() | OrderedSet().union( *(free_unbacked_symbols(e) for e in self.reduction_ranges) ) def get_reduction_size(self) -> Sequence[sympy.Expr]: return self.reduction_ranges def get_reduction_type(self) -> Optional[str]: return self.reduction_type def store_reduction( self, output_name: Optional[str], indexer: Callable[[Sequence[Expr]], Never], vars: Sequence[Expr], reduction_vars: Sequence[Symbol], ) -> None: value = ops.reduction( self.dtype, self.src_dtype, self.reduction_type, self.inner_fn(vars, reduction_vars), ) return ops.store_reduction(output_name or "unnamed", indexer(vars), value) def index_length(self) -> int: return len(self.ranges) + len(self.reduction_ranges) def inner_fn_args(self) -> Sequence[Sequence[Expr]]: index = self._index(self.ranges) rindex = self._index(self.reduction_ranges, SymT.R0_INDEX) return (index, rindex) def inner_fn_free_unbacked_symbols(self) -> OrderedSet[Symbol]: index = self._index(self.ranges) rindex = self._index(self.reduction_ranges, SymT.R0_INDEX) return extract_free_unbacked_symbols(self.inner_fn, index, rindex) def constant_to_device(self, device: torch.device) -> IRNode: """Move this to a given device. Requires that all reads are to constants.""" loader = self.make_loader() loader = patch.object(ConstantBuffer, "override_device", device)(loader) return Reduction( device=device, dtype=self.dtype, inner_fn=loader, ranges=self.ranges, reduction_ranges=self.reduction_ranges, reduction_type=self.reduction_type, src_dtype=self.src_dtype, reduction_hint=ReductionHint.DEFAULT, ) @staticmethod def num_splits( device: torch.device, dst_dtype: torch.dtype, src_dtype: torch.dtype, inner_fn: Callable[..., OpsValue], ranges: Sequence[_IntLike], reduction_ranges: Sequence[_IntLike], reduction_type: Union[ReductionType, Literal["scan"]], reduction_numel: Expr, input_node: Optional[IRNode] = None, ) -> tuple[ReductionHint, _IntLike]: def _is_static(x: object) -> bool: return isinstance(x, (int, Integer)) reduction_numel_hint = V.graph.sizevars.symbolic_hint(reduction_numel) numel_hint = V.graph.sizevars.symbolic_hint(sympy_product(ranges)) should_split = reduction_type == "scan" or ( not V.graph.has_feature(device, BackendFeature.REDUCE_TO_SINGLE_ELEMENT) and reduction_type not in ( "argmax", "argmin", ) and config.split_reductions ) if not (_is_static(reduction_numel_hint) and _is_static(numel_hint)): # We don't support unbacked symints return ReductionHint.DEFAULT, 1 props = DeviceProperties.create(device) num_sm = props.multi_processor_count min_elements_per_thread = 32 if should_split: inner_reduction_splits: Callable[[int, int], int] = functools.partial( V.choices.reduction_split_factor, device, inner_reduction=True ) outer_reduction_splits: Callable[[int, int], int] = functools.partial( V.choices.reduction_split_factor, device, inner_reduction=False ) else: def inner_reduction_splits( reduction_numel_hint: int, numel_hint: int, ) -> int: return 1 outer_reduction_splits = inner_reduction_splits # easy cases if numel_hint == 1: split = inner_reduction_splits(reduction_numel_hint, numel_hint) if split == 1: # No need to split. return ReductionHint.INNER, split if input_node is not None and isinstance(input_node, TensorBox): with patch.object(FlexibleLayout, "allow_indexing", True): ( new_ranges, new_reduction_ranges, ) = extract_input_node_reduction_ranges(input_node) if new_ranges is not None and new_reduction_ranges is not None: extracted_numel_hint = V.graph.sizevars.symbolic_hint( sympy_product(new_ranges + new_reduction_ranges) ) if reduction_numel_hint == extracted_numel_hint: log.debug( "Use previous IRNode's range and reduction_ranges instead of split. " "current ranges: %s, current reduction ranges: %s, current split: %d, " "new ranges: %s, new reduction ranges: %s", ranges, reduction_ranges, split, new_ranges, new_reduction_ranges, ) # If the input_node or its dependent nodes are also Reduction nodes, # use reduction_sizes of this node or its dependent nodes directly. return ReductionHint.INNER, -1 return ReductionHint.INNER, split if ( reduction_numel_hint <= min_elements_per_thread or numel_hint >= num_sm * 2 * 32 ): return ReductionHint.DEFAULT, 1 r = Reduction( device=device, dtype=dst_dtype, inner_fn=inner_fn, ranges=ranges, reduction_ranges=reduction_ranges, reduction_type=reduction_type if reduction_type != "scan" else "sum", src_dtype=src_dtype, reduction_hint=ReductionHint.DEFAULT, ) def get_read_indices(r: Reduction) -> tuple[Sequence[Expr], bool]: cb = ComputedBuffer( name=None, layout=FlexibleLayout( device=r.get_device(), dtype=r.get_dtype(), size=r.get_size(), ), data=r, ) read_writes = cb.get_read_writes() # try finding the full size producer # TODO this will fail for something like ((1, N) * (N, 1)).sum() # this would also possibly be wrong for producers with the different contiguity but we hope those cases are rare assert read_writes.range_vars is not None range_vars = [ r for r in read_writes.range_vars if isinstance(r, Expr) and not isinstance(r, sympy.Number) ] indices = [] changed = False for md in sorted(read_writes.reads, key=lambda x: x.name): if all(r in md.index.free_symbols for r in range_vars): indices.append(md.index) if md.name in V.graph.name_to_buffer: buf = V.graph.name_to_buffer[md.name] original_stride = getattr(buf.layout, "stride", None) buf.decide_layout() if getattr(buf.layout, "stride", None) != original_stride: changed = True return indices, changed indices, changed = get_read_indices(r) if changed: indices, _ = get_read_indices(r) if len(indices) == 0: # TODO determine splits when all inputs are broadcast return ReductionHint.DEFAULT, 1 (_, reduction_vars), ranges1 = dependencies.index_vars_squeeze( r.get_size(), r.get_reduction_size() ) num_outer = 0 num_inner = 0 for i in indices: j = V.graph.sizevars.simplify_with_ranges(i, ranges1) strides = V.graph.sizevars.stride_hints( j, reduction_vars, list(ranges1.keys()) ) outer = all(s > 1 for s in strides) if outer: num_outer += 1 else: num_inner += 1 if num_inner > num_outer: return ReductionHint.INNER, inner_reduction_splits( reduction_numel_hint, numel_hint ) else: return ReductionHint.OUTER, outer_reduction_splits( reduction_numel_hint, numel_hint ) @staticmethod def _unroll_reduction_fn( inner_fn: Callable[[Sequence[_IntLike], Sequence[_IntLike]], OpsValue], reduction_ranges: Sequence[_IntLike], reduction_type: str, src_dtype: torch.dtype, ) -> Callable[[Sequence[_IntLike]], OpsValue]: """Convert inner_fn from a reduction to an pointwise""" reduction_ranges = [ V.graph.sizevars.evaluate_static_shape(x) for x in reduction_ranges ] combine_fn = get_reduction_combine_fn(reduction_type, src_dtype) def fn(index: Sequence[_IntLike]) -> Any: return functools.reduce( combine_fn, ( value_fn(index, rindex) for rindex in itertools.product( *[range(x) for x in reduction_ranges] ) ), ) value_fn: Callable[[Sequence[_IntLike], Sequence[_IntLike]], Any] if reduction_type in ("argmin", "argmax"): flatten_index = FixedLayout( None, # type: ignore[arg-type] None, # type: ignore[arg-type] reduction_ranges, FlexibleLayout.contiguous_strides(reduction_ranges), ).make_indexer() def value_fn( index: Sequence[_IntLike], rindex: Sequence[_IntLike] ) -> tuple[OpsValue, OpsValue]: rindex = [sympy.expand(i) for i in rindex] return ( inner_fn(index, rindex), ops.index_expr(flatten_index(rindex), torch.int64), ) return lambda index: fn(index)[1] else: value_fn = inner_fn return fn @classmethod def create( cls, device: torch.device, dst_dtype: torch.dtype, src_dtype: torch.dtype, inner_fn: Callable[..., Any], ranges: Sequence[Expr], reduction_ranges: Sequence[Expr], reduction_type: ReductionType, reduction_hint: ReductionHint = ReductionHint.DEFAULT, input_node: Optional[IRNode] = None, ) -> TensorBox: reduction_numel = V.graph.sizevars.simplify(sympy_product(reduction_ranges)) if reduction_numel == 0: # N.B. This is a hack to generate the literal of the given type # Ideally, we should be fixing `def constant` in triton.py # but it breaks due to hardcoded dtypes in other places def py_cnst(val: object) -> Union[bool, float, int]: if dst_dtype == torch.bool: return bool(val) elif dst_dtype.is_floating_point: assert isinstance(val, typing.SupportsFloat) return float(val) else: assert isinstance(val, typing.SupportsInt) return int(val) rtypes_to_inits = { "sum": py_cnst(0), "xor_sum": py_cnst(0), "prod": py_cnst(1), "any": py_cnst(0), # "all" is desugared to `!any(!val)` } assert reduction_type in rtypes_to_inits.keys(), ( f"{reduction_type} not supported for zero-dimension tensors!" ) def const_fn(index: int) -> OpsValue: return ops.constant(rtypes_to_inits[reduction_type], dst_dtype) return Pointwise.create( device=device, dtype=src_dtype, inner_fn=const_fn, ranges=list(ranges), ) if reduction_numel == 1: # this reduction is actually a pointwise op if reduction_type in ("argmin", "argmax"): def fn(index: int) -> OpsValue: return ops.constant(0, dst_dtype) else: def fn(index: int) -> OpsValue: reduction_index = [sympy.S.Zero for _ in reduction_ranges] return inner_fn(index, reduction_index) return Pointwise.create( device=device, dtype=dst_dtype, inner_fn=fn, ranges=ranges ) if ( isinstance(reduction_numel, Integer) and V.graph.sizevars.size_hint(reduction_numel) < config.unroll_reductions_threshold and (sympy_product(ranges) != 1 or is_gpu(device.type)) ): # NB: This works around https://github.com/pytorch/pytorch/issues/140457 # since turning reductions into pointwise ops can exacerbate this problem return Pointwise.create( device=device, dtype=dst_dtype, inner_fn=cls._unroll_reduction_fn( inner_fn, reduction_ranges, reduction_type, src_dtype ), ranges=ranges, ) # triton doesn't support reduce to single element well, so break it up hint, split = cls.num_splits( device, dst_dtype, src_dtype, inner_fn, ranges, reduction_ranges, reduction_type, reduction_numel, input_node, ) # intermediate reduction in split can contain complex indexing, # and num_splits will fail to correctly set the hint # reuse the passed hint if available if reduction_hint == ReductionHint.DEFAULT: reduction_hint = hint if split == -1: assert input_node is not None new_ranges, new_reduction_ranges = extract_input_node_reduction_ranges( input_node ) assert new_ranges is not None assert new_reduction_ranges is not None return cls.create_multilayer_existing_ranges( device, dst_dtype, src_dtype, inner_fn, ranges, reduction_ranges, new_ranges, new_reduction_ranges, reduction_type, reduction_hint, ) elif split > 1: # triton doesn't support reduce to single element well, so break it up return cls.create_multilayer( device, dst_dtype, src_dtype, inner_fn, ranges, reduction_ranges, reduction_type, split, reduction_hint, ) return TensorBox.create( Reduction( device=device, dtype=dst_dtype, inner_fn=inner_fn, ranges=ranges, reduction_ranges=reduction_ranges, reduction_type=reduction_type, src_dtype=src_dtype, reduction_hint=reduction_hint, ) ) @staticmethod def default_accumulator( reduction_type: str, dtype: torch.dtype ) -> Union[_NumLike, Sequence[_NumLike]]: if reduction_type in ("max", "argmax"): if is_float_dtype(dtype): return float("-inf") elif is_boolean_dtype(dtype): return False else: return torch.iinfo(dtype).min if reduction_type in ("min", "argmin"): if is_float_dtype(dtype): return float("inf") elif is_boolean_dtype(dtype): return True else: return torch.iinfo(dtype).max zero = False if is_boolean_dtype(dtype) else 0 one = True if is_boolean_dtype(dtype) else 1 return { "sum": zero, "prod": one, "xor_sum": zero, "any": zero, "welford_reduce": (zero, zero, zero), "welford_combine": (zero, zero, zero), "online_softmax_reduce": (float("-inf"), zero), }[reduction_type] @staticmethod def default_value( reduction_type: str, dtype: torch.dtype ) -> Union[_NumLike, Sequence[_NumLike]]: if reduction_type == "welford_reduce": return 0 return Reduction.default_accumulator(reduction_type, dtype) @staticmethod def _multilayer_second_step_hint( split: _IntLike, numel_hint: int, reduction_hint: ReductionHint ) -> ReductionHint: if split == -1: return reduction_hint if split <= 512 and numel_hint <= 512 and reduction_hint == ReductionHint.OUTER: return ReductionHint.OUTER_TINY if ( split <= 1024 and numel_hint <= 256 and reduction_hint == ReductionHint.OUTER ): return ReductionHint.OUTER_TINY return reduction_hint @classmethod def _multilayer_wrap_loader( cls, loader: Callable[..., OpsValue], reduction_ranges: Sequence[_IntLike], reduction_numel: _IntLike, split: _IntLike, block_size: _IntLike, default: Union[_NumLike, Sequence[_NumLike]], ) -> Callable[..., object]: reindex = View.dynamic_reshape_indexer(reduction_ranges, [reduction_numel]) need_mask = not V.graph.sizevars.is_expr_static_and_true( sympy.Eq(reduction_numel % split, 0) ) def wrapper_fn( index: Sequence[Symbol], reduction_index: Sequence[Symbol] ) -> OpsValue: (reduction_index,) = reduction_index *new_index, reduction_block = index indices = block_size * reduction_block + reduction_index def body() -> OpsValue: return loader(new_index, reindex([indices])) if need_mask: mask = ops.lt( ops.index_expr(indices, torch.int32), ops.index_expr(reduction_numel, torch.int32), ) return ops.masked(mask, body, default) else: return body() return wrapper_fn @classmethod def _multilayer_wrap_loader_existing_ranges( cls, loader: Callable[[Sequence[sympy.Expr], Sequence[sympy.Expr]], OpsValue], original_ranges: Sequence[Expr], original_reduction_ranges: Sequence[Expr], new_ranges: Sequence[Integer], new_reduction_ranges: Sequence[Integer], ) -> Callable[[Sequence[sympy.Expr], Sequence[sympy.Expr]], OpsValue]: assert all(r == 1 for r in original_ranges), ( f"Only enabled for numel_hint == 1, found {original_ranges=}" ) reindex = View.dynamic_reshape_indexer( original_reduction_ranges, tuple(new_ranges) + tuple(new_reduction_ranges) ) def wrapper_fn( merged_index: Sequence[sympy.Expr], new_reduction_index: Sequence[sympy.Expr], ) -> OpsValue: original_idx = merged_index[: len(original_ranges)] new_index = merged_index[len(original_ranges) :] return loader( original_idx, reindex(tuple(new_index) + tuple(new_reduction_index)), ) return wrapper_fn @classmethod def create_multilayer_helper( cls, device: torch.device, dst_dtype: torch.dtype, src_dtype: torch.dtype, wrapper_fn: Callable[..., Any], original_ranges: Sequence[Expr], original_reduction_ranges: Sequence[Expr], new_ranges: list[Expr], new_reduction_ranges: list[Integer], reduction_type: ReductionType, split: _IntLike, reduction_hint: ReductionHint, ) -> TensorBox: """ Break a large reduction up into multiple smaller reductions recursively """ # triton will automatically compute reductions in fp32 if reducing over fp16/bf16 # within the kernel. keep the intermediate in fp32 so as to keep the whole reduction # in fp32 and not reduce precision by breaking up the kernel into multiple layers intermediate_dtype = ( dst_dtype if dst_dtype not in (torch.float16, torch.bfloat16) else torch.float ) intermediate = Reduction.create( device, intermediate_dtype, src_dtype, wrapper_fn, new_ranges, new_reduction_ranges, reduction_type, reduction_hint, ) intermediate.realize() intermediate_loader = intermediate.make_loader() def intermediate_fn( index: Sequence[_IntLike], reduction_index: Sequence[_IntLike] ) -> OpsValue: return intermediate_loader([*index, *reduction_index]) numel_hint = V.graph.sizevars.size_hint(sympy_product(original_ranges)) reduction_hint = cls._multilayer_second_step_hint( split, numel_hint, reduction_hint ) assert original_ranges == new_ranges[: len(original_ranges)] return TensorBox.create( Reduction( device=device, dtype=dst_dtype, inner_fn=intermediate_fn, ranges=original_ranges, reduction_ranges=new_ranges[len(original_ranges) :], reduction_type=reduction_type, src_dtype=src_dtype, reduction_hint=reduction_hint, ) ) @classmethod def create_multilayer( cls, device: torch.device, dst_dtype: torch.dtype, src_dtype: torch.dtype, inner_fn: Callable[..., Any], ranges: Sequence[Expr], reduction_ranges: Sequence[Expr], reduction_type: ReductionType, split: _IntLike, reduction_hint: ReductionHint, ) -> TensorBox: """ Break a large reduction up into multiple smaller reductions recursively """ # TODO(jansel): realize the reduction so we can do dynamic indexing reduction_numel = sympy_product(reduction_ranges) block_size = FloorDiv(reduction_numel + (split - 1), split) default = cls.default_value(reduction_type, dst_dtype) wrapper_fn = cls._multilayer_wrap_loader( inner_fn, reduction_ranges, reduction_numel, split, block_size, default ) return cls.create_multilayer_helper( device, dst_dtype, src_dtype, wrapper_fn, ranges, reduction_ranges, [*ranges, split], [block_size], reduction_type, split, reduction_hint, ) @classmethod def create_multilayer_existing_ranges( cls, device: torch.device, dst_dtype: torch.dtype, src_dtype: torch.dtype, inner_fn: Callable[..., Any], original_ranges: Sequence[Expr], original_reduction_ranges: Sequence[Expr], new_ranges: list[Integer], new_reduction_ranges: list[Integer], reduction_type: ReductionType, reduction_hint: ReductionHint, ) -> TensorBox: """ Break a large reduction up into multiple smaller reductions recursively """ wrapper_fn = cls._multilayer_wrap_loader_existing_ranges( inner_fn, original_ranges, original_reduction_ranges, new_ranges, new_reduction_ranges, ) return cls.create_multilayer_helper( device, dst_dtype, src_dtype, wrapper_fn, original_ranges, original_reduction_ranges, [*original_ranges, *new_ranges], new_reduction_ranges, reduction_type, -1, reduction_hint, ) INNER_FN_TY = Callable[[Sequence[Expr], Sequence[Expr]], OpsValue] class MultiOutputReduction(Reduction): output_index: int def __init__( self, device: torch.device, dst_dtype: torch.dtype, inner_fns: Union[INNER_FN_TY, Sequence[INNER_FN_TY]], ranges: Sequence[Integer], reduction_ranges: Sequence[Integer], reduction_type: ReductionType, src_dtype: torch.dtype, reduction_hint: ReductionHint, output_index: int, ): if callable(inner_fns): inner_fns = (inner_fns,) loader: Callable[[Sequence[Expr], Sequence[Expr]], Any] if len(inner_fns) == 1: loader = inner_fns[0] else: def loader( idx: Sequence[Expr], reduction_idx: Sequence[Expr] ) -> tuple[OpsValue, ...]: return tuple(fn(idx, reduction_idx) for fn in inner_fns) super().__init__( device=device, dtype=dst_dtype, inner_fn=loader, ranges=ranges, reduction_ranges=reduction_ranges, reduction_type=reduction_type, src_dtype=src_dtype, reduction_hint=reduction_hint, ) self.output_index = output_index def store_reduction( self, output_name: Optional[str], indexer: Callable[[Sequence[Expr]], Never], vars: Sequence[Expr], reduction_vars: Sequence[Symbol], ) -> None: values = ops.reduction( self.dtype, self.src_dtype, self.reduction_type, self.inner_fn(vars, reduction_vars), ) assert isinstance(values, (tuple, list)), f"{type(values)}" value = values[self.output_index] return ops.store_reduction(output_name or "unnamed", indexer(vars), value) class OnlineSoftmaxReduction(MultiOutputReduction): @classmethod def create( # type: ignore[override] cls, device: torch.device, dst_dtype: torch.dtype, src_dtype: torch.dtype, inner_fn: Callable[..., Any], ranges: Sequence[Expr], reduction_ranges: Sequence[Expr], num_output: int, reduction_hint: ReductionHint = ReductionHint.DEFAULT, input_node: Optional[IRNode] = None, ) -> Sequence[TensorBox]: """ Create the reduction disregarding splitting. """ results = tuple( TensorBox.create( MultiOutputReduction( device, dst_dtype, inner_fn, ranges, reduction_ranges, "online_softmax_reduce", # type: ignore[arg-type] src_dtype, reduction_hint, output_idx, ) ) for output_idx in range(num_output) ) for t in results: t.realize() return results class WelfordReduction(MultiOutputReduction): @classmethod def create( # type: ignore[override] cls, device: torch.device, dtype: torch.dtype, inner_fns: Sequence[Callable[..., Any]], ranges: list[Integer], reduction_ranges: list[Integer], reduction_type: ReductionType, reduction_hint: ReductionHint = ReductionHint.DEFAULT, ) -> Sequence[TensorBox]: assert reduction_type in ("welford_reduce", "welford_combine") reduction_numel = V.graph.sizevars.simplify(sympy_product(reduction_ranges)) def const(val: int) -> TensorBox: def inner_fn(idx: Sequence[Expr]) -> OpsValue: return ops.constant( val, dtype, ) return Pointwise.create( device=device, dtype=dtype, inner_fn=inner_fn, ranges=list(ranges), ) if reduction_numel == 0: mean = const(0) m2 = const(0) weight = const(0) return mean, m2, weight if reduction_numel == 1: def copy( loader: Callable[[Sequence[Expr], Sequence[Expr]], OpsValue], ) -> TensorBox: def inner_fn(idx: Sequence[Expr]) -> OpsValue: reduction_index = [sympy.S.Zero for _ in reduction_ranges] return loader(idx, reduction_index) return Pointwise.create( device=device, dtype=dtype, inner_fn=inner_fn, ranges=list(ranges), ) if reduction_type == "welford_reduce": return copy(inner_fns[0]), const(0), const(1) else: return tuple(copy(fn) for fn in inner_fns) # TODO: Unrolled reduction # if ( # isinstance(reduction_numel, Integer) # and V.graph.sizevars.size_hint(reduction_numel) # < config.unroll_reductions_threshold # and sympy_product(ranges) != 1 # ): # return Pointwise.create( # device, # dst_dtype, # cls._unroll_reduction_fn( # inner_fn, reduction_ranges, reduction_type, src_dtype, # ), # ranges, # ) # triton doesn't support reduce to single element well, so break it up hint, split = Reduction.num_splits( device, dtype, dtype, inner_fns[0], ranges, reduction_ranges, reduction_type=reduction_type, reduction_numel=reduction_numel, ) # intermediate reduction in split can contain complex indexing, # and num_splits will fail to correctly set the hint # reuse the passed hint if available if reduction_hint == ReductionHint.DEFAULT: reduction_hint = hint if split > 1: # triton doesn't support reduce to single element well, so break it up return cls.create_multilayer( device, dtype, inner_fns, ranges, reduction_ranges, reduction_type, split, reduction_hint, ) results = [ TensorBox.create( WelfordReduction( device, dtype, inner_fns, ranges, reduction_ranges, reduction_type, dtype, reduction_hint, output_idx, ) ) for output_idx in range(3) ] for t in results: t.realize() return results @staticmethod def default_value( reduction_type: str, dtype: torch.dtype ) -> Union[_NumLike, Sequence[_NumLike]]: return (0, 0, 0) @classmethod def create_multilayer( # type: ignore[override] cls, device: torch.device, dtype: torch.dtype, inner_fns: Sequence[Callable[..., Any]], ranges: list[Integer], reduction_ranges: list[Integer], reduction_type: ReductionType, split: _IntLike, reduction_hint: ReductionHint, ) -> Sequence[TensorBox]: """ Break a large reduction up into multiple smaller reductions recursively """ reduction_numel = sympy_product(reduction_ranges) need_mask = not V.graph.sizevars.is_expr_static_and_true( sympy.Eq(reduction_numel % split, 0) ) if need_mask and reduction_type != "welford_combine": # If we need mask, then "welford_reduce" doesn't work because # masked inputs shouldn't count towards the welford weight def constant( idx: Sequence[Expr], reduction_idx: Sequence[Expr], value: int ) -> OpsValue: return ops.constant(value, dtype) return cls.create_multilayer( device=device, dtype=dtype, inner_fns=( inner_fns[0], partial(constant, value=0), partial(constant, value=1), ), ranges=ranges, reduction_ranges=reduction_ranges, reduction_type="welford_combine", split=split, reduction_hint=reduction_hint, ) block_size = FloorDiv(reduction_numel + (split - 1), split) intermediates = WelfordReduction.create( device, dtype, tuple( cls._multilayer_wrap_loader( loader, reduction_ranges, reduction_numel, split, block_size, default=0, ) for loader in inner_fns ), [*ranges, split], [block_size], reduction_type, reduction_hint, ) for i in intermediates: i.realize() def intermediate_loader_fn( index: Sequence[Expr], reduction_index: Sequence[Expr], loader: Callable[[Sequence[Expr]], OpsValue], ) -> OpsValue: return loader([*index, *reduction_index]) numel_hint = V.graph.sizevars.size_hint(sympy_product(ranges)) reduction_hint = cls._multilayer_second_step_hint( split, numel_hint, reduction_hint ) return WelfordReduction.create( device, dtype, tuple( partial(intermediate_loader_fn, loader=i.make_loader()) for i in intermediates ), ranges, [split], # welford_reduce turns one input into three outputs, which are combined with welford_combine "welford_combine", reduction_hint, ) @ir_dataclass class Scan(Loops): scan_ranges: list[Integer] size: list[Integer] combine_fn: Callable[[tuple[Any, ...], tuple[Any, ...]], tuple[Any, ...]] reindex: Callable[[Sequence[_IntLike], Sequence[_IntLike]], Sequence[_IntLike]] reduction_hint: ReductionHint output_index: int # output_index indexes the following tuples dtypes: tuple[torch.dtype, ...] inner_fns: tuple[Callable[..., Any], ...] # HACK we mimick reduction def get_unbacked_symbol_uses(self) -> OrderedSet[Symbol]: # TODO: Can combine_fn/reindex close over unbacked symbols? If so, we # need to explicitly represent the closure so we can pull out unbacked # symbols here return ( super().get_unbacked_symbol_uses() | OrderedSet().union(*(free_unbacked_symbols(e) for e in self.scan_ranges)) | OrderedSet().union(*(free_unbacked_symbols(e) for e in self.size)) ) def __post_init__(self) -> None: assert len(self.ranges) + len(self.scan_ranges) == len(self.size) super().__post_init__() def store_reduction( self, output_name: Optional[str], indexer: Callable[[Sequence[_IntLike]], Never], vars: Sequence[Expr], scan_vars: Sequence[Symbol], ) -> None: idx = self.reindex(vars, scan_vars) values = tuple(inner_fn(idx) for inner_fn in self.inner_fns) result = ops.scan(self.dtypes, self.combine_fn, values) return ops.store( output_name or "unnamed", indexer(idx), result[self.output_index] ) def get_reduction_type(self) -> Optional[str]: # return self.scan_op return "custom" def get_reduction_size(self) -> Sequence[sympy.Expr]: return self.scan_ranges def get_size(self) -> Sequence[Expr]: return self.size def get_pointwise_size(self) -> Sequence[Expr]: return self.ranges def index_length(self) -> int: return len(self.ranges) + len(self.scan_ranges) def inner_fn_args(self) -> Sequence[Sequence[_IntLike]]: index = self._index(self.ranges) rindex = self._index(self.scan_ranges, SymT.R0_INDEX) idx = self.reindex(index, rindex) return (idx,) def inner_fn_free_unbacked_symbols(self) -> OrderedSet[Symbol]: index = self._index(self.ranges) rindex = self._index(self.scan_ranges, SymT.R0_INDEX) idx = self.reindex(index, rindex) return extract_free_unbacked_symbols(self.inner_fn, idx) @classmethod def create( # type: ignore[override] cls, device: torch.device, dtypes: tuple[torch.dtype, ...], inner_fns: tuple[Callable[[Sequence[Expr]], Any], ...], size: list[Integer], axis: int, combine_fn: Callable[[tuple[Any, ...], tuple[Any, ...]], tuple[Any, ...]], reduction_hint: ReductionHint = ReductionHint.DEFAULT, *, # Whether we have the option to fallback to aten can_fallback_to_aten: bool = True, **kwargs: Any, ) -> Sequence[Optional[TensorBox]]: pointwise_ranges = [*size[:axis], *size[axis + 1 :]] scan_ranges = [size[axis]] if not V.graph.has_feature(device, BackendFeature.SCAN): return [None] * len(dtypes) if len(dtypes) > 1 and not V.graph.has_feature( device, BackendFeature.TUPLE_REDUCTION ): return [None] * len(dtypes) sizevars = V.graph.sizevars scan_numel = sizevars.simplify(sympy_product(scan_ranges)) assert len(dtypes) == len(inner_fns) # Scan with a single element is just a copy if sizevars.is_expr_static_and_true(sympy.Le(scan_numel, 1)): return [ Pointwise.create( device=device, dtype=dtypes[output_index], inner_fn=inner_fns[output_index], ranges=size, ) for output_index in range(len(dtypes)) ] reduction_hint, num_splits = cls.num_splits( device=device, dtype=dtypes[0], inner_fn=inner_fns[0], axis=axis, pointwise_ranges=pointwise_ranges, scan_ranges=scan_ranges, combine_fn=combine_fn, scan_numel=scan_numel, ) scan_type = Scan if num_splits > 1: supports_split = ( torch.version.hip is None or (has_triton and triton_version >= "3.3.0") ) and (len(dtypes) == 1) if not supports_split: if can_fallback_to_aten: # Fallback to ATen return [None] * len(dtypes) else: num_splits = 1 else: scan_type = SplitScan def reindex(index: Sequence[Expr], scan_index: Sequence[Expr]) -> list[Expr]: assert len(scan_index) == len(scan_ranges) assert len(index) == len(pointwise_ranges) return [*index[:axis], *scan_index, *index[axis:]] results = [ TensorBox.create( scan_type( device=device, dtype=dtypes[output_index], dtypes=dtypes, inner_fn=inner_fns[output_index], inner_fns=inner_fns, size=size, ranges=pointwise_ranges, scan_ranges=scan_ranges, combine_fn=combine_fn, reindex=reindex, reduction_hint=reduction_hint, output_index=output_index, **kwargs, ) ) for output_index in range(len(dtypes)) ] for result in results: result.realize() return results @classmethod def num_splits( cls, device: torch.device, dtype: torch.dtype, inner_fn: Callable[[Sequence[Expr]], OpsValue], axis: int, pointwise_ranges: list[Integer], scan_ranges: list[Integer], combine_fn: Callable[[tuple[Any, ...], tuple[Any, ...]], tuple[Any, ...]], scan_numel: Expr, ) -> tuple[ReductionHint, _IntLike]: # TODO: custom splitting heuristic for scan def wrapper_fn(idx: Sequence[Expr], reduction_idx: Sequence[Expr]) -> OpsValue: return inner_fn([*idx[:axis], *reduction_idx, *idx[axis:]]) return Reduction.num_splits( device=device, dst_dtype=dtype, src_dtype=dtype, inner_fn=wrapper_fn, ranges=pointwise_ranges, reduction_ranges=scan_ranges, reduction_type="scan", reduction_numel=scan_numel, ) # This signifies a scan op that should go through TritonSplitScanKernel codegen on CUDA. @ir_dataclass class SplitScan(Scan): pass @ir_dataclass class Sort(Loops): # Sorts a tuple of key, value pairs sort_ranges: list[Integer] size: list[Integer] reindex: Callable[[Sequence[Expr], Sequence[Expr]], Sequence[Expr]] reduction_hint: ReductionHint output_index: int # output_index indexes the following tuples dtypes: tuple[torch.dtype, ...] inner_fns: tuple[Callable[..., Any], ...] stable: bool descending: bool # HACK we mimick reduction def get_unbacked_symbol_uses(self) -> OrderedSet[Symbol]: return ( super().get_unbacked_symbol_uses() | OrderedSet().union(*(free_unbacked_symbols(e) for e in self.sort_ranges)) | OrderedSet().union(*(free_unbacked_symbols(e) for e in self.size)) ) def __post_init__(self) -> None: assert len(self.ranges) + len(self.sort_ranges) == len(self.size) super().__post_init__() def store_reduction( self, output_name: Optional[str], indexer: Callable[[Sequence[Expr]], Expr], vars: Sequence[Expr], reduction_vars: Sequence[Expr], ) -> None: idx = self.reindex(vars, reduction_vars) values = tuple(inner_fn(idx) for inner_fn in self.inner_fns) result = ops.sort(self.dtypes, values, self.stable, self.descending) return ops.store( output_name or "unnamed", indexer(idx), result[self.output_index] ) def get_reduction_type(self) -> Optional[str]: return "sort" def get_reduction_size(self) -> Sequence[Expr]: return self.sort_ranges def get_size(self) -> Sequence[Expr]: return self.size def get_pointwise_size(self) -> Sequence[Expr]: return self.ranges def index_length(self) -> int: return len(self.ranges) + len(self.sort_ranges) def inner_fn_args(self) -> Sequence[Sequence[Expr]]: index = self._index(self.ranges) rindex = self._index(self.sort_ranges, SymT.R0_INDEX) idx = self.reindex(index, rindex) return (idx,) def inner_fn_free_unbacked_symbols(self) -> OrderedSet[Symbol]: index = self._index(self.ranges) rindex = self._index(self.sort_ranges, SymT.R0_INDEX) idx = self.reindex(index, rindex) return extract_free_unbacked_symbols(self.inner_fn, idx) @classmethod def create( # type: ignore[override] cls, device: torch.device, dtypes: tuple[torch.dtype, ...], inner_fns: tuple[Callable[[list[Expr]], Any], ...], size: list[Integer], axis: int, stable: bool, descending: bool, reduction_hint: ReductionHint = ReductionHint.DEFAULT, **kwargs: Any, ) -> Sequence[Optional[TensorBox]]: pointwise_ranges = [*size[:axis], *size[axis + 1 :]] sort_ranges = [size[axis]] if not V.graph.has_feature(device, BackendFeature.SORT): return [None] * len(dtypes) sizevars = V.graph.sizevars sort_numel = sizevars.simplify(sympy_product(sort_ranges)) # Heuristic, smallest rblock where triton usually outperforms aten.sort # It also isn't bandwidth bound so fusion is unlikely to help. max_rblock = 512 is_persistent_kernel = ( config.triton.persistent_reductions and sizevars.is_expr_static_and_true(sympy.Le(sort_numel, max_rblock)) ) if not is_persistent_kernel: # We only support persistent triton kernels return [None] * len(dtypes) assert len(dtypes) == len(inner_fns) # Sort with a single element is just a copy if sizevars.is_expr_static_and_true(sympy.Le(sort_numel, 1)): return [ Pointwise.create( device=device, dtype=dtypes[output_index], inner_fn=inner_fns[output_index], ranges=size, ) for output_index in range(len(dtypes)) ] def reindex(index: Sequence[Expr], sort_index: Sequence[Expr]) -> list[Expr]: assert len(sort_index) == len(sort_ranges) assert len(index) == len(pointwise_ranges) return [*index[:axis], *sort_index, *index[axis:]] results = [ TensorBox.create( Sort( device=device, dtype=dtypes[output_index], dtypes=dtypes, inner_fn=inner_fns[output_index], inner_fns=inner_fns, size=size, ranges=pointwise_ranges, sort_ranges=sort_ranges, reindex=reindex, reduction_hint=reduction_hint, output_index=output_index, stable=stable, descending=descending, **kwargs, ) ) for output_index in range(len(dtypes)) ] for result in results: result.realize() return results def is_storage_and_layout(x: IRNode) -> bool: try: as_storage_and_layout(x, freeze=False) return True except NotImplementedError: return False def is_contiguous_storage_and_layout(x: IRNode) -> bool: try: _buffer, layout = as_storage_and_layout(x, freeze=False) # pad the stride here so we will NOT claim an tensor as contiguous # if a padding is gonna happen. if layout.should_pad_strides(): layout.pad_strides() return layout.is_contiguous() except NotImplementedError: return False def as_storage_and_layout( x: IRNode, freeze: bool = True, want_contiguous: bool = False, stride_order: Optional[Sequence[Union[int, Integer]]] = None, allow_padding: bool = False, exact_strides: Optional[Sequence[Union[int, Integer]]] = None, ) -> tuple[StorageBox, Layout]: """ Try to simplify x into a StorageBox and a Layout. allow_padding only affect how we apply stride_order. When allow_padding is True, we have the freedom to add padding when applying the stride_order. """ if isinstance(x, TensorBox): return as_storage_and_layout( x.data, freeze=freeze, want_contiguous=want_contiguous, stride_order=stride_order, allow_padding=allow_padding, exact_strides=exact_strides, ) if isinstance(x, StorageBox): _, layout = as_storage_and_layout( x.data, freeze=freeze, want_contiguous=want_contiguous, stride_order=stride_order, allow_padding=allow_padding, exact_strides=exact_strides, ) return x, x.data.get_layout() if isinstance(x, Buffer): if freeze: if want_contiguous: x.freeze_layout() assert x.get_layout().is_contiguous() elif stride_order is not None: x.freeze_layout_with_stride_order( stride_order, allow_padding=allow_padding ) elif exact_strides is not None: x.freeze_layout_with_exact_strides( exact_strides, allow_padding=allow_padding ) else: x.decide_layout() return StorageBox(x), x.get_layout() if isinstance(x, ReinterpretView): # making the base of x contiguous or stride_ordered will not necessarily make # the ReinterpretView either, so don't pass along those arguments buffer, _ = as_storage_and_layout( x.data, freeze=freeze, ) return buffer, x.layout raise NotImplementedError def is_stride_order_storage_and_layout( x: IRNode, stride_order: Sequence[Union[int, Integer]] ) -> bool: try: _buffer, layout = as_storage_and_layout(x, freeze=False) return layout.is_stride_ordered(stride_order) except NotImplementedError: return False @ir_dataclass class BaseView(IRNode): data: IRNode def get_unbacked_symbol_uses(self) -> OrderedSet[Symbol]: return self.data.get_unbacked_symbol_uses() def make_reindexer(self) -> Callable[[Sequence[Expr]], Sequence[Expr]]: raise NotImplementedError(f"make_reindexer NYI on {self}") def make_indexer(self) -> Callable[[Sequence[Expr]], Expr]: inner = self.data.make_indexer() reindex = self.make_reindexer() def indexer(idx: Sequence[Expr]) -> Expr: return inner(reindex(idx)) return indexer def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: inner = self.data.make_loader() reindex = self.make_reindexer() def loader(idx: Sequence[Expr]) -> OpsValue: return inner(reindex(idx)) return loader @property def dtype(self) -> torch.dtype: return self.data.get_dtype() def get_layout(self) -> Layout: return self.data.get_layout() def get_device(self) -> Optional[torch.device]: return self.data.get_device() def get_origin_node(self) -> Optional[torch.fx.Node]: return None def get_name(self) -> str: return self.data.get_name() def get_pointwise_size(self) -> Sequence[Expr]: return self.get_size() def mark_reuse(self, users: int) -> None: return self.data.mark_reuse(users) def has_exceeded_max_reads(self) -> bool: return self.data.has_exceeded_max_reads() def realize(self) -> Optional[str]: return self.data.realize() def realize_hint(self): # type: ignore[no-untyped-def] return self.data.realize_hint() def get_storage_numel(self): # type: ignore[no-untyped-def] return self.data.get_storage_numel() def is_extern(self) -> bool: return self.data.is_extern() # type: ignore[attr-defined] def is_module_buffer(self) -> bool: return self.data.is_module_buffer() # type: ignore[attr-defined] def get_read_names(self) -> OrderedSet[str]: return self.data.get_read_names() def get_reads(self) -> OrderedSet[Dep]: with patch.object(FlexibleLayout, "allow_indexing", True): return extract_read_writes( self.make_loader(), self.get_size(), # type: ignore[arg-type] ).reads def unwrap_view(self): # type: ignore[no-untyped-def] x: IRNode = self while isinstance(x, BaseView): x = x.data return x def constant_to_device(self, device: torch.device) -> IRNode: """Move this to a given device. Requires that all reads are to constants.""" loader = self.make_loader() loader = patch.object(ConstantBuffer, "override_device", device)(loader) return Pointwise( device=device, dtype=self.get_dtype(), inner_fn=loader, ranges=self.get_size(), ) @ir_dataclass class ExpandView(BaseView): size: list[Expr] @staticmethod def _normalize_size(x, new_size): # type: ignore[no-untyped-def] """Replace `-1` with correct sizes""" sizevars = V.graph.sizevars new_size = list(map(sympy.expand, new_size)) old_size = x.get_size() old_size = [None] * (len(new_size) - len(old_size)) + list(old_size) assert len(new_size) == len(old_size) for i in range(len(new_size)): if new_size[i] == -1: assert old_size[i] is not None new_size[i] = old_size[i] elif old_size[i] is None or V.graph.sizevars.shape_env.evaluate_expr( sympy.Eq(old_size[i], 1), size_oblivious=True ): pass else: # Sanity check: Expect broadcast compatibility # # NB: new_size[i] == old_size[i] is expected to already be # guarded because the meta formula was expected to have taught # us this equality. assert sizevars.size_hint(new_size[i] - old_size[i], fallback=0) == 0, ( "Broadcast failed in ExpandView({x.get_size()}, {new_size}) on dimension {i}" ) return new_size @classmethod def create(cls, x, new_size): # type: ignore[no-untyped-def] new_size = cls._normalize_size(x, new_size) if is_storage_and_layout(x): storage, old_layout = as_storage_and_layout(x) skip = len(new_size) - len(old_layout.size) assert skip >= 0 new_stride = [sympy.S.Zero] * skip for stride, size in zip(old_layout.stride, old_layout.size): new_stride.append( stride if not V.graph.sizevars.shape_env.evaluate_expr( sympy.Eq(size, 1), size_oblivious=True ) else sympy.S.Zero ) new_layout = FixedLayout( old_layout.device, old_layout.dtype, list(new_size), new_stride, old_layout.offset, ) return ReinterpretView(data=storage, layout=new_layout) return ExpandView(data=x, size=new_size) def get_size(self) -> Sequence[Expr]: return self.size def make_reindexer(self): # type: ignore[no-untyped-def] target = self.get_size() actual = self.data.get_size() skip = len(target) - len(actual) def reindex(index): # type: ignore[no-untyped-def] index = list(index[skip:]) assert len(index) == len(actual) for i in range(len(actual)): if actual[i] == 1: # zero out broadcast dimension index[i] = sympy.S.Zero return index return reindex @ir_dataclass class PermuteView(BaseView): dims: list[Expr] @classmethod def create(cls, x, dims): # type: ignore[no-untyped-def] dims = cls._map_neg_dims(dims) assert OrderedSet(dims) == OrderedSet(range(len(dims))) if is_storage_and_layout(x): storage, old_layout = as_storage_and_layout(x) new_layout = FixedLayout( old_layout.device, old_layout.dtype, [old_layout.size[i] for i in dims], [old_layout.stride[i] for i in dims], old_layout.offset, ) return ReinterpretView(data=storage, layout=new_layout) return PermuteView(data=x, dims=dims) @classmethod def _map_neg_dims(cls, dims): # type: ignore[no-untyped-def] return [dim if dim >= 0 else len(dims) + dim for dim in dims] def get_size(self) -> Sequence[Expr]: assert OrderedSet(self._map_neg_dims(self.dims)) == OrderedSet( range(len(self.dims)) ) size = self.data.get_size() return [size[i] for i in self.dims] def make_reindexer(self): # type: ignore[no-untyped-def] inv = {j: i for i, j in enumerate(self.dims)} inv = [inv[i] for i in range(len(self.dims))] assert OrderedSet(inv) == OrderedSet(range(len(self.dims))) def reindex(index): # type: ignore[no-untyped-def] return [index[i] for i in inv] return reindex @ir_dataclass class SqueezeView(BaseView): @classmethod def create(cls, x, *, dim=None): # type: ignore[no-untyped-def] if is_storage_and_layout(x): storage, old_layout = as_storage_and_layout(x) new_size = [] new_stride = [] if dim is not None: assert isinstance(dim, int), "expected integer dim argument" assert 0 <= dim and dim < len(old_layout.size) for i, (size, stride) in enumerate(zip(old_layout.size, old_layout.stride)): if dim is None: if size != 1: new_size.append(size) new_stride.append(stride) else: if i != dim: new_size.append(size) new_stride.append(stride) else: assert size == 1, "expected squeezed size to be 1" new_layout = FixedLayout( old_layout.device, old_layout.dtype, new_size, new_stride, old_layout.offset, ) return ReinterpretView(data=storage, layout=new_layout) if dim is None: # redirect to a generic view return View.create(x, [s for s in x.get_size() if s != 1]) else: assert x.get_size()[dim] == 1 return View.create(x, [s for i, s in enumerate(x.get_size()) if i != dim]) @staticmethod def squeezer(size: Sequence[sympy.Expr]): # type: ignore[no-untyped-def] new_size = [s for s in size if s != 1] not_one = [i for i, s in enumerate(size) if s != 1] length = len(size) def reindex(index: list[sympy.Expr]) -> tuple[sympy.Expr, ...]: assert len(index) == len(not_one), f"{index} {not_one}" new_index = [sympy.S.Zero] * length for idx, s in zip(not_one, index): new_index[idx] = s return tuple(new_index) return new_size, reindex def __init__(self, data) -> None: # type: ignore[no-untyped-def] raise AssertionError("use SqueezeView.create()") @ir_dataclass class GenericView(BaseView): size: list[Expr] reindex: Callable[..., Any] def make_reindexer(self): # type: ignore[no-untyped-def] return self.reindex def reindex_str(self) -> str: index_old = [ sympy_index_symbol_with_prefix(SymT.INDEX, n) for n in range(len(self.size)) ] index_new = list(self.reindex(index_old)) return f"lambda {', '.join(map(str, index_old))}: {index_new}" def __str__(self) -> str: return self.str_helper( [self.data, f"size={self.size}", f"reindex={self.reindex_str()}"] ) __repr__ = __str__ @classmethod def create(cls, x, new_size, reindex): # type: ignore[no-untyped-def] return cls(data=x, size=list(new_size), reindex=reindex) def get_size(self) -> Sequence[Expr]: return self.size @ir_dataclass class View(GenericView): @staticmethod def handle_negative_index(idx, size): # type: ignore[no-untyped-def] idx = sympy.expand(idx) size = sympy.expand(size) evaluate_expr = V.graph.sizevars.shape_env.evaluate_expr if evaluate_expr(sympy.Lt(idx, 0)): idx = idx + size return idx @classmethod def create(cls, x, new_size): # type: ignore[no-untyped-def] assert isinstance(new_size, (tuple, list)) old_size, new_size = cls.resolve_negative_size(x.get_size(), new_size) # Skip pointless views if V.graph.sizevars.statically_known_list_equals(old_size, new_size): return x unbacked_symbols_in_sizes = False if ( len(free_unbacked_symbols(old_size)) > 0 or len(free_unbacked_symbols(new_size)) > 0 ): unbacked_symbols_in_sizes = True if 0 in new_size: def fake_reindex(index): # type: ignore[no-untyped-def] return tuple([0] * len(old_size)) return cls(data=x, size=list(new_size), reindex=fake_reindex) # TODO: a new class for FixedTransferLayout that output layout is constrained by input layout elif is_contiguous_storage_and_layout(x) or unbacked_symbols_in_sizes: if unbacked_symbols_in_sizes and (not is_contiguous_storage_and_layout(x)): # realize x; otherwise, the dynamic_reshape_indexer below will fail # due to the size_hint's inability to process unbacked SymInts # TODO: unbacked should not diverge from backed in determining striding # Need to require contiguous here instead of realize, see: # https://github.com/pytorch/pytorch/issues/145561 x = ExternKernel.require_exact_strides( x, FlexibleLayout.contiguous_strides(x.get_size()) ) storage, old_layout = as_storage_and_layout(x, want_contiguous=True) new_layout = FixedLayout( old_layout.device, old_layout.dtype, new_size, FlexibleLayout.contiguous_strides(new_size), old_layout.offset, ) return ReinterpretView(data=storage, layout=new_layout) reindex = cls.dynamic_reshape_indexer(old_size, new_size) return cls(data=x, size=list(new_size), reindex=reindex) @staticmethod def resolve_negative_size(old_size, new_size): # type: ignore[no-untyped-def] new_size = [V.graph.sizevars.simplify(x) for x in new_size] old_size = [V.graph.sizevars.simplify(x) for x in old_size] new_size = list(new_size) for i in range(len(new_size)): if new_size[i] == -1: new_size[i] = sympy.S.One new_size[i] = CleanDiv(sympy_product(old_size), sympy_product(new_size)) break V.graph.sizevars.guard_equals(sympy_product(old_size), sympy_product(new_size)) return old_size, new_size @classmethod def dynamic_reshape_indexer(cls, old_size, new_size): # type: ignore[no-untyped-def] try: reindex = cls._dynamic_reshape_indexer(old_size, new_size) except (AssertionError, IndexError): # optimistic algorithm failed, lets do a fallback flat = [sympy_product(old_size)] reindex1 = cls._dynamic_reshape_indexer(old_size, flat) reindex2 = cls._dynamic_reshape_indexer(flat, new_size) reindex = fuse_reindexing(reindex1, reindex2) return reindex @staticmethod def _dynamic_reshape_indexer(old_size, new_size): # type: ignore[no-untyped-def] """ Perform a reshape entirely by modifying indexing math """ size_hint = V.graph.sizevars.size_hint # TODO: These symbols may not escape, if they don't assert so and # treat them as temporary vars = [ sympy_index_symbol_with_prefix(SymT.VIEW, i) for i in range(len(new_size)) ] stack_new = list(zip(vars, new_size)) stack_old = list(old_size) view_expr = [] while stack_new and stack_old: size_old = stack_old.pop() var, size_new = stack_new.pop() if size_old == 1: view_expr.append(sympy.S.Zero) stack_new.append((var, size_new)) # re-add elif size_new == 1: stack_old.append(size_old) # re-add elif size_hint(size_new) == size_hint(size_old): view_expr.append(var) V.graph.sizevars.guard_equals(size_new, size_old) elif size_hint(size_new) < size_hint(size_old): while size_hint(size_new) < size_hint(size_old): var2, size_new2 = stack_new.pop() var = var2 * size_new + var size_new = size_new * size_new2 view_expr.append(var) V.graph.sizevars.guard_equals(size_new, size_old) elif size_hint(size_new) > size_hint(size_old): divisor = sympy.S.One modulus = size_old view_expr.append(ModularIndexing(var, divisor, modulus)) divisor = divisor * modulus while size_hint(size_new) > size_hint(size_old): modulus = stack_old.pop() view_expr.append(ModularIndexing(var, divisor, modulus)) divisor = divisor * modulus size_old = size_old * modulus V.graph.sizevars.guard_equals(size_new, size_old) else: raise AssertionError while stack_old: size_old = stack_old.pop() V.graph.sizevars.guard_equals(size_old, 1) view_expr.append(sympy.S.Zero) while stack_new: var, size_new = stack_new.pop() V.graph.sizevars.guard_equals(size_new, 1) view_expr.reverse() assert len(view_expr) == len(old_size) def reindex(index): # type: ignore[no-untyped-def] assert len(index) == len(vars), (len(index), len(vars)) replacements = dict(zip(vars, index)) return tuple(sympy_subs(x, replacements) for x in view_expr) return reindex @ir_dataclass class ReinterpretView(BaseView): """Pretend our storage has a different layout""" layout: Layout def __post_init__(self) -> None: super().__post_init__() if isinstance(self.data, BaseView): object.__setattr__(self, "data", self.data.unwrap_view()) def __str__(self) -> str: return self.str_helper( [ self.data, self.layout, ] ) __repr__ = __str__ def get_name(self): # type: ignore[no-untyped-def] return self.data.get_name() def get_device(self) -> Optional[torch.device]: return self.layout.device def get_origin_node(self) -> Optional[torch.fx.Node]: return None @property def dtype(self): # type: ignore[no-untyped-def] return self.layout.dtype def get_size(self) -> Sequence[Expr]: return list(self.layout.size) def get_stride(self): # type: ignore[no-untyped-def] return list(self.layout.stride) def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: def loader(index: Sequence[Expr]) -> OpsValue: indexer = self.layout.make_indexer() tmp_loader = ops.load(self.get_name(), indexer(index)) if self.layout.dtype != self.data.dtype: return ops.to_dtype_bitcast(tmp_loader, self.dtype, self.data.dtype) else: return tmp_loader return loader def make_indexer(self) -> Callable[[Sequence[Expr]], Expr]: return self.layout.make_indexer() def get_layout(self) -> Layout: return self.layout def freeze_layout(self): # type: ignore[no-untyped-def] pass def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: return ( free_unbacked_symbols(self.layout.size) | free_unbacked_symbols(self.layout.stride) | free_unbacked_symbols(self.layout.offset) ) def codegen_reference(self, writer: Optional[IndentedBuffer] = None) -> str: # reinterpret_tensor is similar to as_strided except: # - offset is added to the existing offset (rather than replacing it) # - view tracking is disabled similar to unsafe_view return V.graph.wrapper_code.codegen_reinterpret_view( self.data, self.layout.size, self.layout.stride, self.layout.offset, writer.writeline if writer is not None else V.graph.wrapper_code.writeline, dtype=self.layout.dtype, ) def num_reads(self) -> int: return 1 @ir_dataclass class DtypeView(BaseView): """Pretend our storage has a different type""" target_dtype: torch.dtype @classmethod def create(cls, x, new_dtype): # type: ignore[no-untyped-def] if is_storage_and_layout(x): storage, old_layout = as_storage_and_layout(x) new_layout = FixedLayout( old_layout.device, new_dtype, old_layout.size, old_layout.stride, old_layout.offset, ) return ReinterpretView(data=storage, layout=new_layout) return DtypeView(data=x, target_dtype=new_dtype) def __str__(self) -> str: return self.str_helper([self.data, self.target_dtype]) __repr__ = __str__ @property def dtype(self): # type: ignore[no-untyped-def] return self.target_dtype def get_size(self) -> Sequence[Expr]: return self.data.get_size() def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: inner = self.data.make_loader() def loader(idx): # type: ignore[no-untyped-def] return ops.to_dtype_bitcast(inner(idx), self.target_dtype, self.data.dtype) return loader class SliceView(View): @classmethod def normalize_start_end(cls, x, dim, start, end): # type: ignore[no-untyped-def] """ Normalize start and end such that both are in the range [0, x.get_size()[dim]] and start <= end. """ sizevars = V.graph.sizevars dim_size = x.get_size()[dim] if any(free_unbacked_symbols(x) for x in (start, end, dim_size)): min_func = sympy.Min max_func = sympy.Max else: min_func = sizevars.evaluate_min max_func = sizevars.evaluate_max def clamp(x, lower, upper): # type: ignore[no-untyped-def] clamped_lower = ( x if sizevars.statically_known_geq(x, lower) else max_func(x, lower) ) clamped_full = ( clamped_lower if sizevars.statically_known_leq(clamped_lower, upper) else min_func(clamped_lower, upper) ) return clamped_full def clamp_wrap(val, lower, upper, default): # type: ignore[no-untyped-def] if val is None: return default val = cls.handle_negative_index(val, dim_size) return clamp(val, lower, upper) start = clamp_wrap(start, 0, dim_size, 0) end = clamp_wrap(end, start, dim_size, dim_size) return start, end @classmethod def create(cls, x, dim, start, end, step=1, clamp=True): # type: ignore[no-untyped-def] step = sympy.expand(step) assert isinstance(step, sympy.Expr) or step > 0 try: if start == 0 and end >= 2**63 - 1 and step == 1: return x except TypeError: pass new_size = list(x.get_size()) # NB: Ordinarily we default to clamping. # We only don't clamp for split_with_sizes. For split_with_sizes, sizes should be already valid # failing in this situation is ok, since invalid sizes could trigger silent errors. if clamp: start, end = cls.normalize_start_end(x, dim, start, end) new_size[dim] = FloorDiv(end - start + (step - 1), step) if is_storage_and_layout(x): # Fast path storage, old_layout = as_storage_and_layout(x) new_stride = list(old_layout.stride) new_stride[dim] = new_stride[dim] * step new_layout = FixedLayout( old_layout.device, old_layout.dtype, new_size, new_stride, old_layout.offset + old_layout.stride[dim] * start, ) return ReinterpretView(data=storage, layout=new_layout) def reindex(index): # type: ignore[no-untyped-def] assert len(index) == len(new_size), f"wrong ndim {index} {new_size}" index = list(index) index[dim] = index[dim] * step + start return index # redirect to a generic view return SliceView(data=x, size=new_size, reindex=reindex) @ir_dataclass class BaseConstant(IRNode): dtype: torch.dtype device: torch.device def get_size(self) -> Sequence[Expr]: return () def get_device(self) -> Optional[torch.device]: return self.device def get_origin_node(self) -> Optional[torch.fx.Node]: return None def get_reads(self) -> OrderedSet[Dep]: return OrderedSet() @ir_dataclass class Constant(BaseConstant): value: Any dtype: torch.dtype device: torch.device def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: def loader(index: Sequence[Expr]) -> OpsValue: return ops.constant(self.value, self.dtype) return loader def realize(self) -> Optional[str]: pass def constant_to_device(self, device: torch.device) -> IRNode: return Constant(value=self.value, dtype=self.dtype, device=device) @ir_dataclass class IndexingConstant(BaseConstant): index: Any dtype: torch.dtype device: torch.device def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: def loader(index: Sequence[Expr]) -> OpsValue: return ops.index_expr(self.index, self.dtype) return loader def constant_to_device(self, device: torch.device) -> IRNode: return IndexingConstant(index=self.index, dtype=self.dtype, device=device) def is_contiguous_strides_for_shape( stride: Sequence[_IntLike], shape: Sequence[_IntLike] ) -> bool: return all( size == 1 or left == right for left, right, size in zip( stride, FlexibleLayout.contiguous_strides(shape), shape ) ) def get_align_for_dtype(dtype: torch.dtype) -> int: return config.padding_alignment_bytes // dtype.itemsize class OutputSpec: """Abstract base for Layout, MultiOutputLayout, NoneLayout. Represents the memory layout of the output of an Operation.""" def get_device(self) -> Optional[torch.device]: raise NotImplementedError(type(self).__name__) def storage_size(self) -> int: raise NotImplementedError(type(self).__name__) @ir_dataclass class Layout(OutputSpec): def __init__( self, device: torch.device, dtype: torch.dtype, size: list[Expr], stride: Optional[list[Expr]] = None, offset: Expr = Integer(0), ) -> None: if stride is None: stride = FlexibleLayout.contiguous_strides(size) self.device = device self.dtype = dtype assert len(size) == len(stride), f"size={size}, stride={stride}" assert all(isinstance(s, (Expr, int)) for s in size) self.size: list[Expr] = size self.stride: list[Expr] = stride self.offset: Expr = offset def __str__(self) -> str: offset = "" if self.offset != 0: offset = f", offset={self.offset}" device_index_str = "" if self.device.index is None else f":{self.device.index}" return ( f"{type(self).__name__}('{self.device.type}{device_index_str}', {self.dtype}, " f"size={self.size}, stride={self.stride}{offset})" ) __repr__ = __str__ def get_device(self) -> torch.device: return self.device def is_contiguous(self) -> bool: return is_contiguous_strides_for_shape(self.stride, self.size) @staticmethod def is_channels_last_contiguous( shape: Sequence[_IntLike], strides: Sequence[_IntLike] ) -> bool: ndim = len(shape) if ndim not in [4, 5] or shape[1] == 1: return False for left, right, size in zip( strides, make_channels_last_strides_for(shape), shape ): if size != 1 and left != right: return False return True def is_transposed(self) -> bool: for left, right, size in zip( self.stride, reversed(FlexibleLayout.contiguous_strides(list(reversed(self.size)))), self.size, ): if size != 1 and left != right: return False return True def is_stride_ordered(self, order) -> bool: # type: ignore[no-untyped-def] assert len(self.stride) == len(order) # ignore dimensions of size 1, they dont affect layout non_1_indices = [ i for i, dim in enumerate(self.size) if V.graph.sizevars.size_hint(dim, fallback=2) != 1 ] stride = [self.stride[i] for i in non_1_indices] order = [order[i] for i in non_1_indices] def sorted_indices(arr): # type: ignore[no-untyped-def] sorted_arr = sorted(arr) return [sorted_arr.index(element) for element in arr] # since we may have removed dimensions, need to re-sort & re-index order order = sorted_indices(order) # reorder the stride given order stride_ordered = [-1] * len(order) for i in range(len(order)): stride_ordered[order[i]] = stride[i] # check if it is in ascending order for i in range(len(order) - 1): expr = stride_ordered[i] > stride_ordered[i + 1] if not isinstance(expr, bool): expr = V.graph._shape_env.evaluate_expr( stride_ordered[i] > stride_ordered[i + 1], size_oblivious=True ) if expr: return False return True def is_channels_last_stride_ordered(self): # type: ignore[no-untyped-def] # create channels_last order(NCHW, NCDHW, the C is the first order). order = [0] + list(reversed(range(1, len(self.stride) - 1))) order = [len(order)] + order return self.is_stride_ordered(order) @staticmethod def _pad_strides(in_strides, size, dtype): # type: ignore[no-untyped-def] """ The padding does not change stride order but makes sure all strides larger than the threshold are multiple of align. """ align = get_align_for_dtype(dtype) if len(in_strides) == 0: return in_strides if not config.pad_channels_last and Layout.is_channels_last_contiguous( size, in_strides ): return in_strides current_fx_node = V.get_current_node() if hasattr(current_fx_node, "meta") and current_fx_node.meta.get( "dislike_padding", False ): return in_strides # get_stride_order does not work with dynamic shape. Also we can not # statically decide if a padding is needed or how much padding we should # do for dynamic shape. # # Skip padding the strides for dynamic shape for now. if not all( isinstance(s, (int, sympy.Integer)) for s in itertools.chain(in_strides, size) ): return in_strides stride_order = get_stride_order(in_strides) fill_order = stride_order2fill_order(stride_order) new_strides = [0 for _ in range(len(in_strides))] # since we pad when the layout is flexible, we can decide the # smallest stride to be 1. new_strides[fill_order[0]] = 1 padded = False for rank, idx in enumerate(fill_order[1:], start=1): prev_idx = fill_order[rank - 1] stride = new_strides[prev_idx] * size[prev_idx] if stride > config.padding_stride_threshold and stride % align != 0: stride = ceildiv(stride, align) * align padded = True new_strides[idx] = stride if not padded: # Consider a tensor with shape [256, 1, 5, 5] # Avoid strides like [25, 5, 5, 1] being padded to equivalent strides # [25, 25, 5, 1]. return in_strides metrics.num_comprehensive_padding += 1 return new_strides def pad_strides(self): # type: ignore[no-untyped-def] assert isinstance(self, FlexibleLayout) assert self.stride is not None self.stride = self._pad_strides(self.stride, self.size, self.dtype) def should_pad_strides(self): # type: ignore[no-untyped-def] return config.comprehensive_padding and isinstance(self, FlexibleLayout) def as_fixed(self): # type: ignore[no-untyped-def] if isinstance(self, FixedLayout): return self if self.should_pad_strides(): self.pad_strides() return FixedLayout( self.device, self.dtype, self.size, self.stride, self.offset, ) def make_indexer(self) -> Callable[[Sequence[Expr]], Expr]: assert FlexibleLayout.allow_indexing, ( f"convert {type(self).__name__} to FixedLayout first" ) return self.as_fixed().make_indexer() def __eq__(self, other) -> bool: # type: ignore[no-untyped-def] return ( self.device == other.device and self.dtype == other.dtype and self.size == other.size and self.stride == other.stride and self.offset == other.offset ) def storage_size(self) -> sympy.Expr: return compute_required_storage_length(self.size, self.stride, self.offset) class FixedLayout(Layout): """A Tensor layout we cannot change""" def make_indexer(self) -> Callable[[Sequence[Expr]], Expr]: """A closure containing math to read a given element""" def indexer(index): # type: ignore[no-untyped-def] assert len(index) == len(self.stride) assert len(index) == len(self.size) result = self.offset for idx, stride, sz in zip(index, self.stride, self.size): if sz != 1: result = result + idx * stride return result return indexer class FlexibleLayout(Layout): """A Tensor layout we are allowed to change""" allow_indexing = False # WARNING! This doesn't handle zero size tensors correctly @staticmethod def contiguous_strides(sizes): # type: ignore[no-untyped-def] if len(sizes) == 0: return [] reversed_strides = [sympy.S.One] for size in reversed(sizes[1:]): reversed_strides.append(size * reversed_strides[-1]) return list(reversed(reversed_strides)) @staticmethod def fill_ordered(sizes, order): # type: ignore[no-untyped-def] """ Create a stride based on the order the dimensions should be filled in. In this format, channels last would be: [1, 3, 2, 0] """ assert OrderedSet(range(len(sizes))) == OrderedSet(order), (sizes, order) next_stride = sympy.S.One strides = [None] * len(order) for i in order: strides[i] = next_stride next_stride = next_stride * sizes[i] return strides @staticmethod def stride_ordered(sizes, order): # type: ignore[no-untyped-def] """ Create a stride based on the sorted order of a permuted range. In this format, channels last would be: [3, 0, 2, 1] """ assert OrderedSet(range(len(sizes))) == OrderedSet(order) fill_order = stride_order2fill_order(order) return FlexibleLayout.fill_ordered(sizes, fill_order) @staticmethod def stride_ordered_for_memory_format(sizes, memory_format): # type: ignore[no-untyped-def] """ Create a stride based on a memory format. Memory format is translasted into a stride order, so channels_last is the same as: FlexibleLayout.stride_ordered(sizes, [3, 0, 2, 1]) This interface does not support memory_format `torch.preserve_format` which should be used to deduce a format from another source """ if memory_format == torch.channels_last: return FlexibleLayout.stride_ordered(sizes, NHWC_STRIDE_ORDER) elif memory_format == torch.channels_last_3d: return FlexibleLayout.stride_ordered(sizes, NHWDC_STRIDE_ORDER) elif memory_format == torch.contiguous_format: return FlexibleLayout.contiguous_strides(sizes) else: log.debug( "stride_ordered_for_memory_format, unsuppored memory_format: %s", memory_format, ) raise NotImplementedError @staticmethod def same_ordered(sizes, stride): # type: ignore[no-untyped-def] """ Create a stride that has the same stride order as given stride For example, if given stride is [1000, 1, 100, 10], the fill order should be [1, 3, 2, 0] """ assert len(sizes) == len(stride) stride = [V.graph.sizevars.size_hint(x) for x in stride] fill_order = sorted(range(len(stride)), key=stride.__getitem__) return FlexibleLayout.fill_ordered(sizes, fill_order) def as_stride_order(self, order, allow_padding=False): # type: ignore[no-untyped-def] new_stride = self.stride_ordered(self.size, order) if self.should_pad_strides() and allow_padding: new_stride = self._pad_strides(new_stride, self.size, self.dtype) return FixedLayout( self.device, self.dtype, self.size, new_stride, self.offset, ) def as_exact_strides(self, exact_strides, allow_padding=False): # type: ignore[no-untyped-def] new_stride = exact_strides if self.should_pad_strides() and allow_padding: new_stride = self._pad_strides(new_stride, self.size, self.dtype) return FixedLayout( self.device, self.dtype, self.size, new_stride, self.offset, ) def as_fill_order(self, order): # type: ignore[no-untyped-def] new_stride = self.fill_ordered(self.size, order) if self.should_pad_strides(): new_stride = self._pad_strides(new_stride, self.size, self.dtype) return FixedLayout( self.device, self.dtype, self.size, new_stride, self.offset, ) def as_same_order(self, stride): # type: ignore[no-untyped-def] new_stride = self.same_ordered(self.size, stride) if self.should_pad_strides(): new_stride = self._pad_strides(new_stride, self.size, self.dtype) return FixedLayout( self.device, self.dtype, self.size, new_stride, self.offset, ) def __init__(self, device, dtype, size, stride_order=None) -> None: # type: ignore[no-untyped-def] if stride_order: strides = FlexibleLayout.fill_ordered(size, stride_order) else: strides = FlexibleLayout.contiguous_strides(size) super().__init__(device, dtype, size, strides) class NonOwningLayout(Layout): """Is a view into the storage of another tensor""" def __init__(self, view: Union[BaseView, TensorBox]) -> None: layout = view.get_layout() super().__init__( layout.device, layout.dtype, layout.size, layout.stride, ) self.view = view def make_indexer(self) -> Callable[[Sequence[Expr]], Expr]: return self.as_fixed().make_indexer() def maybe_guard_aligned(self): # type: ignore[no-untyped-def] offset = self.view.get_layout().offset if offset == 0: return True from .utils import ALIGNMENT return V.graph.sizevars.statically_known_multiple_of(offset, ALIGNMENT) class CommBufferType(Enum): SYMM_MEM = "symm_mem" class CommBufferLayout(FixedLayout): """ A layout that signifies the buffer is a comm buffer. In terms of striding, the layout is identical to `FixedLayout`. Buffers with this layout do not participate in in-place reuse - it can be neither the source nor the target for in-place reuse. For detailed motivation and usage of this layout, see NOTE [lowering-time collective optimization]. """ comm_buffer_type: CommBufferType group_name: str def __init__( self, layout: FlexibleLayout, comm_buffer_type: CommBufferType, group_name: str, ): if not isinstance(layout, FlexibleLayout): raise AssertionError( "A `CommBufferLayout` can only be initialized with " f"a `FlexibleLayout` (got {layout})." ) fixed = layout.as_fixed() super().__init__( device=fixed.device, dtype=fixed.dtype, size=fixed.size, stride=fixed.stride, offset=fixed.offset, ) self.comm_buffer_type = comm_buffer_type self.group_name = group_name @ir_dataclass class NoneLayout(OutputSpec): # This is janky, I figured out what fields to populate by just running # the model I was interested in and adding properties/methods as needed. # This doesn't inherit from Layout because Layout assumes you have stuff # like sizes, but I don't really have anything here. # # If you have an ir.Node with NoneLayout, you probably need to setup # dependencies manually in scheduler device: Optional[torch.device] size: list[int] = dataclasses.field(default_factory=lambda: [0]) stride: list[int] = dataclasses.field(default_factory=lambda: [0]) def storage_size(self) -> int: return 0 def as_fixed(self): # type: ignore[no-untyped-def] return self def get_device(self) -> Optional[torch.device]: return self.device class MutationLayoutSHOULDREMOVE(Layout): def __init__(self, target: IRNode) -> None: super().__init__( target.get_device_or_error(), target.get_dtype(), target.get_size(), # type: ignore[arg-type] None, ) self.target = target name = self.get_buffer().get_name() V.graph.mark_buffer_mutated(name) @property def stride(self) -> list[Expr]: return self.real_layout().stride @stride.setter def stride(self, value: Never) -> None: pass # ignore setting of stride def storage_size(self) -> sympy.Expr: return self.real_layout().storage_size() def get_buffer(self) -> Buffer: def unwrap_views(target): # type: ignore[no-untyped-def] if isinstance(target, MutationLayoutSHOULDREMOVE): return unwrap_views(target.target) if isinstance(target, BaseView): return unwrap_views(target.unwrap_view()) if isinstance(target, MutableBox): return unwrap_views(target.data) return target result = unwrap_views(self.target) assert isinstance(result, Buffer), ( "MutationLayoutSHOULDREMOVE must refer to a buffer" ) return result def real_layout(self): # type: ignore[no-untyped-def] return self.get_buffer().layout @classmethod def realize_into(cls, src, dst, unsafe_alias=False): # type: ignore[no-untyped-def] dst.realize() # NOTE: We must realize users of `dst` before we realize `src`, since # realization order determines scheduling order. Otherwise, src's # mutation would be scheduled before the existing users of dst! V.graph.mark_buffer_mutated(dst.get_name()) if isinstance(src, TensorBox): src = src.data # We copy the contents of src into dst. In most cases this should # be fused into a single kernel by the scheduler. # NOTE: We cannot change src's layout to mutate dst directly as this # would alias src to dst, which is not correct as further mutations to # dst would effect users of src. However if there are no more users of # dst, we can alias src to dst. src.realize_hint() if not unsafe_alias: src = Pointwise.create( device=src.get_device(), dtype=src.get_dtype(), inner_fn=src.make_loader(), ranges=[ V.graph.sizevars.guard_equals(a, b) for a, b in zip(src.get_size(), dst.get_size()) ], ).data src.realize() assert isinstance(src.data.layout, FlexibleLayout) src.data.layout = MutationLayoutSHOULDREMOVE(dst) return src.data def as_fixed(self): # type: ignore[no-untyped-def] return self def make_indexer(self) -> Callable[[Sequence[Expr]], Expr]: return self.target.make_indexer() @ir_dataclass(frozen=False) class Buffer(IRNode): # Name is sometimes None; e.g., ForceInPlace, where there isn't # a meaningful name name: Optional[str] layout: OutputSpec # Multi-output buffers will define 'outputs: List[Buffer]'. Confusingly, # MultiOutput does NOT define this! def __post_init__(self) -> None: super().__post_init__() self._post_init_setattr("origin_node", None) def make_indexer(self) -> Callable[[Sequence[Expr]], Expr]: return self.get_layout().make_indexer() def get_name(self) -> str: assert self.name, self return self.name def get_device(self) -> Optional[torch.device]: return self.get_output_spec().get_device() def get_defining_op(self) -> Optional[Operation]: return None @property def dtype(self) -> torch.dtype: return self.get_layout().dtype def get_size(self) -> Sequence[Expr]: return [*self.get_layout().size] def get_stride(self) -> list[Expr]: return [*self.get_layout().stride] def get_offset(self) -> Expr: return self.get_layout().offset def get_layout(self) -> Layout: if isinstance(self.layout, Layout): return self.layout raise NotImplementedError(type(self.layout).__name__) def get_output_spec(self) -> OutputSpec: return self.layout def get_storage_numel(self): # type: ignore[no-untyped-def] return self.get_numel() def freeze_layout(self): # type: ignore[no-untyped-def] if isinstance(self.layout, Layout) and not isinstance( self.layout, NonOwningLayout ): self.layout = self.layout.as_fixed() def freeze_layout_with_stride_order(self, order, allow_padding=False) -> None: # type: ignore[no-untyped-def] assert isinstance(self.layout, FlexibleLayout) self.layout = self.layout.as_stride_order(order, allow_padding=allow_padding) def freeze_layout_with_fill_order(self, order) -> None: # type: ignore[no-untyped-def] assert isinstance(self.layout, FlexibleLayout) self.layout = self.layout.as_fill_order(order) def freeze_layout_with_same_order(self, stride) -> None: # type: ignore[no-untyped-def] assert isinstance(self.layout, FlexibleLayout) self.layout = self.layout.as_same_order(stride) def freeze_layout_with_exact_strides( # type: ignore[no-untyped-def] self, exact_strides, allow_padding=False ) -> None: assert isinstance(self.layout, FlexibleLayout) self.layout = self.layout.as_exact_strides( exact_strides, allow_padding=allow_padding ) def is_zero_elements(self): # type: ignore[no-untyped-def] return V.graph.sizevars.is_expr_static_and_true(sympy.Eq(self.get_numel(), 0)) def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: # Loading from a zero-element buffer is a no-op if self.is_zero_elements(): return partial(nop_loader_fn, dtype=self.get_dtype()) def loader(index): # type: ignore[no-untyped-def] indexer = self.make_indexer() return ops.load(self.name or "unnamed", indexer(index)) return loader def codegen_reference(self, writer: Optional[IndentedBuffer] = None) -> str: return self.get_name() def decide_layout(self): # type: ignore[no-untyped-def] pass def get_inputs_that_alias_output(self) -> Sequence[str]: if isinstance(self.layout, NonOwningLayout): return [self.layout.view.get_name()] return () def get_mutation_names(self) -> Sequence[str]: if isinstance(self.layout, MutationLayoutSHOULDREMOVE): return [self.layout.target.get_name()] return () def get_read_names(self) -> OrderedSet[str]: return OrderedSet([self.get_name()]) def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def realize(self) -> Optional[str]: pass def should_allocate(self) -> bool: # Returns False by default. return False @ir_dataclass(frozen=False) class OperationBuffer(Buffer, Operation): # An operation that produces a single output buffer def get_outputs(self) -> list[Buffer]: return [self] def get_defining_op(self) -> Operation: return self # Skip implementation in Buffer get_operation_name = Operation.get_operation_name def __post_init__(self) -> None: Buffer.__post_init__(self) Operation.__post_init__(self) class InputBuffer(Buffer): def num_reads(self) -> int: return 1 class DonatedBuffer(InputBuffer): """ Represents a donated buffer which is a saved tensor that is not alias to any fwd inputs, fwd user outputs, and bwd outputs. We generally cannot inplace reuse the input tensor memory during backward since it might be used in another function. However, donated buffer can be inplace reused during backward to save memory. """ class ConstantBuffer(InputBuffer): override_device: Optional[torch.device] = None def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: def loader(index: Sequence[Expr]) -> OpsValue: indexer = self.get_layout().make_indexer() return ops.load( V.graph.constant_name(self.get_name(), self.override_device), indexer(index), ) return loader def constant_to_device(self, device: torch.device) -> IRNode: return ConstantBuffer( name=V.graph.constant_name(self.get_name(), device), layout=self.layout ) @ir_dataclass class NoneAsConstantBuffer(IRNode): def get_reads(self) -> OrderedSet[Dep]: return OrderedSet() def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def codegen_reference(self, writer: Optional[IndentedBuffer] = None) -> str: return V.graph.wrapper_code.none_str def get_output_spec(self) -> OutputSpec: return NoneLayout(device=None) def has_tensor_output(self) -> bool: return False @ir_dataclass class ShapeAsConstantBuffer(IRNode): expr: Expr def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: return free_unbacked_symbols(self.expr) def codegen_reference(self, writer: Optional[IndentedBuffer] = None) -> str: return V.graph.wrapper_code.codegen_sizevar(self.expr) def has_tensor_output(self) -> bool: return False @ir_dataclass(frozen=False) class ComputedBuffer(OperationBuffer): data: Loops def get_computed_buffer_name(self) -> Optional[str]: """ Returns self.name if it exists, otherwise returns the name of the data node if that exists. If neither exist, returns None. """ if self.name is not None: return self.name if hasattr(self.data, "name"): return self.data.name return None def num_reads(self) -> int: return self.data.num_reads() def get_reads(self) -> OrderedSet[Dep]: return self.data.get_reads() def get_read_names(self) -> OrderedSet[str]: return self.data.get_read_names() def get_read_writes(self) -> dependencies.ReadWrites: with patch.object(FlexibleLayout, "allow_indexing", True): if self.data.get_reduction_type(): return extract_read_writes( self.get_store_function(), self.data.get_pointwise_size(), # type: ignore[arg-type] self.data.get_reduction_size(), # type: ignore[arg-type] ) else: return extract_read_writes( self.get_store_function(), self.data.get_size(), # type: ignore[arg-type] ) def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: # Ordinarily, we'd like to just peek at the arguments list, # but ComputedBuffers have no argument list. # # Morally, this logic needs to be synchronized with the # KernelArgs.size calls, which are responsible for making symbols make # there way as kernel arguments (and it is precisely passing in one of # those symbols that establishes a dependency). However, we haven't # started codegen yet so we can't directly reuse that logic. # # For now, I'm just yoloing with the size of the buffer. Not sure if # it is enough. # # One thing you might wonder is if this is enough for a ComputedBuffer # denoting a reduction over i0. Empirically, it is enough, but for an # unusual reason: we only need accurate dependencies for item() call, # but it's impossible to end up with a reduction over i0 from an # item() call without a regular non-reduction buffer first. return ( free_unbacked_symbols(self.get_size()) | free_unbacked_symbols(self.get_stride()) | free_unbacked_symbols(self.get_offset()) | self.data.get_unbacked_symbol_uses() ) def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: if ( not self.get_reduction_type() and self.name not in V.graph.mutated_buffers and self.num_reads() == 0 ): # inline this op rather than generating ops.load() return self.data.make_loader() return super().make_loader() def get_store_function(self) -> Callable[..., None]: indexer = self.get_layout().as_fixed().make_indexer() if isinstance(self.data, (Reduction, Scan, Sort)): return partial(self.data.store_reduction, self.name, indexer) else: assert isinstance(self.data, Pointwise) return partial(self.data.store_output, self.name, indexer) def get_fill_order(self) -> Optional[list[int]]: """ If our layout is still flexible, try to determine the stride order based on stride orders of reads. TODO(jansel): A better algorithm here would look at downstream consumers of this value and try to do global graph-level layout optimization. This is also something just begging to be autotuned. """ if isinstance(self.layout, FlexibleLayout): (index_vars, reduction_vars), _ = dependencies.index_vars_squeeze( self.data.get_pointwise_size(), self.data.get_reduction_size() ) reads = self.get_read_writes().reads # only consider reads to buffer of same size # ignore StarDeps because they don't contribute stride information assert all( isinstance(r, (dependencies.StarDep, dependencies.MemoryDep)) for r in reads ) reads = [ sympy_subs(r.index, {v: sympy.S.Zero for v in reduction_vars if v != 0}) for r in reads if isinstance(r, dependencies.MemoryDep) ] if reads: if isinstance(self.data, (Scan, Sort)): indices = self.data.reindex(index_vars, reduction_vars) else: indices = index_vars stride_lengths = [ V.graph.sizevars.stride_hints(expr, indices) for expr in reads ] from .scheduler import pick_loop_order return pick_loop_order(stride_lengths, self.get_size()) return None def decide_layout(self) -> None: if isinstance(self.layout, FlexibleLayout): order = self.get_fill_order() if order: self.freeze_layout_with_fill_order(order) else: self.freeze_layout() @cache_on_self def get_default_sizes_body( self, ) -> tuple[ tuple[list[sympy.Expr], list[sympy.Expr]], LoopBody, tuple[list[sympy.Expr], list[sympy.Expr]], ]: args, var_ranges = dependencies.index_vars_squeeze( self.data.get_pointwise_size(), self.data.get_reduction_size(), prefix="q" ) with patch.object(ConstantBuffer, "override_device", self.get_device()): body = LoopBody( self.get_store_function(), (args if self.get_reduction_type() else args[:1]), var_ranges, *args, ) index_vars = [] reduce_vars: list[Any] = [] index_size = [] reduce_size = [] for v, s in var_ranges.items(): if v in args[0]: assert not reduce_vars index_vars.append(v) index_size.append(s) else: assert v in args[1] reduce_vars.append(v) reduce_size.append(s) return (index_size, reduce_size), body, (index_vars, reduce_vars) def simplify_and_reorder( self, extra_indexing_constraints: Optional[tuple[dict[Any, Any], list[Any]]] = None, recompute_sizes_body_func: Optional[Callable[..., Any]] = None, ) -> tuple[tuple[list[sympy.Expr], list[sympy.Expr]], LoopBody]: """ This is a main place where we do loop transformations in a backend-agnostic way. Here we: 1) Remove any 1 dimensions 2) Fuse contiguous dimensions together 3) Reorder dimensions based on stride orders Optional argument extra_indexing_constraints can be used to append additional indexing expressions to existing ones derived from buffer's body. This can be useful to fuse scheduler nodes with compatible ranges, e.g. (s0*s1*...,) and (s0, s1, s2, ...) on CPU by preventing indexing simplifications and obtaining index/reduce ranges for the scheduler node compatible with other nodes. Optional argument recompute_sizes_body_func can be used to recompute sizes and body on the default body. This can be useful to append additional loop transformations. """ ( (index_size, reduce_size), body, (index_vars, reduce_vars), ) = self.get_default_sizes_body() if recompute_sizes_body_func: ( (index_size, reduce_size), body, (index_vars, reduce_vars), ) = recompute_sizes_body_func( (index_size, reduce_size), body, (index_vars, reduce_vars) ) index_formulas = [*body.indexing_exprs.values()] if extra_indexing_constraints is not None: assert ( isinstance(extra_indexing_constraints, tuple) and len(extra_indexing_constraints) == 2 ) extra_indexing_ranges, extra_indexing_expr = extra_indexing_constraints assert isinstance(extra_indexing_ranges, dict) assert isinstance(extra_indexing_expr, list) assert all(isinstance(f, Expr) for f in extra_indexing_expr) expected_var_ranges = body.var_ranges assert expected_var_ranges == extra_indexing_ranges, ( expected_var_ranges, extra_indexing_ranges, ) # remove already existing expressions extra_indexing_expr = [ e for e in extra_indexing_expr if e not in index_formulas ] index_formulas += extra_indexing_expr memory_addrs = [*body.get_write_exprs()] if not V.graph.has_feature(self, BackendFeature.PREFER_STORE_LOOP_ORDER): memory_addrs.extend(body.get_read_exprs()) def simplify_and_reorder(x_vars, support_vars, sizes, simplify_loops): # type: ignore[no-untyped-def] sizes, reindex0, reindex1 = self._apply_loop_reordering( x_vars, support_vars, sizes, memory_addrs ) # for NHWC: reindex0([0,1,2,3]) = [0,2,3,1], reindex1([0,1,2,3]) = [0,3,2,1] x_vars = reindex0(x_vars) if simplify_loops: sizes, reindex2, _prune = V.graph.sizevars._simplify_loops( x_vars, sizes, index_prevent_reordering(index_formulas, x_vars, sizes), ) reindex = fuse_reindexing(reindex1, reindex2) else: reindex = reindex1 return sizes, reindex, reindex1 support_vars = index_vars + reduce_vars should_merge_loops = ( not is_gpu(get_device_type(self)) or not config.loop_ordering_after_fusion ) iter_ranges, iter_reindex, _ = simplify_and_reorder( index_vars, support_vars, index_size, should_merge_loops, ) # Like iteration dimensions, we may also want to delay merging reduction dimensions. # E.g., if we reduce a tensor [M, N, K] for its M and N dimensions followed by a pointwise # kernel, merging M and N dimension too early makes it hard to decide what loop order # we should pick for the piontwise kernel so that it is fusible with the reduction. reduce_ranges, reduce_reindex, _ = simplify_and_reorder( reduce_vars, support_vars, reduce_size, should_merge_loops ) # retrace the loop body with simplification and reordering applied (iter_vars, reduce_vars), var_ranges = dependencies.index_vars_no_squeeze( iter_ranges, reduce_ranges, prefix="p", ) body = LoopBody( body, [iter_reindex(iter_vars), reduce_reindex(reduce_vars)], var_ranges, iter_vars, reduce_vars, ) return (iter_ranges, reduce_ranges), body @staticmethod def _apply_loop_reordering( # type: ignore[no-untyped-def] index_vars, support_vars, sizes, memory_addrs, priority_idx=None, ): """ Shuffle the order of loops around to hopefully improve performance. """ from .scheduler import pick_loop_order if priority_idx is None: priority_idx = [] try: strides = [ V.graph.sizevars.stride_hints(expr, index_vars, support_vars) for expr in memory_addrs ] assert len(strides) == len(memory_addrs) and len(strides[0]) == len( index_vars ) order = list(reversed(pick_loop_order(strides, sizes, priority_idx))) except Exception: if config.debug: log.warning( "Did not simplify complex index:\n%s\n%s", dict(zip(index_vars, sizes)), memory_addrs, ) order = list(range(len(sizes))) sizes = [sizes[i] for i in order] return sizes, same_reorder(order), inverse_reorder(order) def get_reduction_size(self) -> Sequence[sympy.Expr]: return self.data.get_reduction_size() def get_reduction_type(self) -> Optional[str]: return self.data.get_reduction_type() def is_no_op(self) -> bool: return self.data.is_zero_elements() def should_allocate(self) -> bool: return True def constant_to_device(self, device: torch.device) -> IRNode: """Move this to a given device. Requires that all reads are to constants.""" return self.data.constant_to_device(device) class TemplateBuffer(OperationBuffer): """ Represents a Triton (in the future other type) of template operator that we can fuse an epilogue onto. """ def __init__( self, layout: Layout, inputs: Sequence[IRNode], make_kernel_render: Callable[..., Any], ) -> None: super().__init__(name=None, layout=layout) self.inputs = InputsKernel.unwrap_storage(inputs) self.make_kernel_render = make_kernel_render self.name = V.graph.register_buffer(self) V.graph.register_operation(self) def get_read_writes(self) -> dependencies.ReadWrites: return self.extract_read_writes(normalize=True) def extract_read_writes(self, normalize): # type: ignore[no-untyped-def] name = self.get_name() indexer = self.get_layout().make_indexer() def dummy(index, rindex): # type: ignore[no-untyped-def] assert len(rindex) == 0 return ops.store(name, indexer(index), "fake") deps = dependencies.extract_read_writes( dummy, self.get_size(), (), normalize=normalize ) for inp in self.inputs: indexer = inp.layout.make_indexer() def dummy(index, rindex): # type: ignore[no-untyped-def] assert len(rindex) == 0 ops.load(inp.get_name(), indexer(index)) deps.reads |= dependencies.extract_read_writes( dummy, inp.get_size(), (), normalize=True ).reads return deps def get_reduction_size(self) -> Sequence[sympy.Expr]: return sympy.S.One def get_reduction_type(self) -> Optional[str]: return None def should_allocate(self) -> bool: return True def simplify_and_reorder( # type: ignore[no-untyped-def] self, extra_indexing_constraints: Optional[tuple[dict[Any, Any], list[Any]]] = None, recompute_sizes_body_func: Optional[Callable[..., Any]] = None, ): return ( ( self.get_size(), (), ), None, ) class TritonTemplateBuffer(TemplateBuffer): def __init__( # type: ignore[no-untyped-def] self, layout, inputs, make_kernel_render, mutated_inputs: Optional[Iterable[IRNode]] = None, allowed_prologue_inps: Optional[OrderedSet[str]] = None, ) -> None: """ NOTE:[TritonTemplates with multiple outputs] We want the ability for TritonTemplates to output multiple tensors. Triton kernels have no notion of outputs and this is done by creating tensors that are then mutated by the kernel. Currenlty our STORE_OUTPUT codegen doesn't support creating multinode outputs for triton templates. We work around this by creating an extra input buffer during the lowering and we mark them as mutated inputs. """ super().__init__(layout, inputs, make_kernel_render) self.mutated_inputs = mutated_inputs self.outputs: list[Buffer] = [self] if mutated_inputs is not None: # Ensure that the mutated inputs are only allowed for certain nodes allowed_set = ( torch.ops.higher_order.flex_attention, torch.ops.higher_order.flex_attention_backward, ) current_node = V.graph.current_node.target assert current_node in allowed_set, ( f"Mutated inputs are only allowed for {allowed_set} but got {current_node}" ) device = self.inputs[0].get_device() self.outputs += [ MutationOutput(NoneLayout(device=device), buf, self) for buf in mutated_inputs ] self.allowed_prologue_inps = ( allowed_prologue_inps if allowed_prologue_inps else OrderedSet() ) def get_outputs(self) -> list[Buffer]: return self.outputs def get_allowed_prologue_inps(self) -> OrderedSet[str]: return self.allowed_prologue_inps def __str__(self) -> str: out = f"TritonTemplateBuffer(layout={self.layout})" return out PrimitiveInfoType = Union[int, float, bool, str, list[Union[int, str, float, bool]]] class ChoiceCaller: """ Represents a possible choice used in autotune_process.py. During autotuning, self.benchmark() is first called to get benchmark result, and if this choice is selected, self.output_node() is called to get the output_node. Children classes: TritonTemplateCaller, CUDATemplateCaller. """ def __init__( self, name: str, input_nodes: list[Buffer], layout: Layout, description: str, ) -> None: super().__init__() self.name = name self.layout = layout self.input_nodes = input_nodes # An additional description used to describe the choice (useful for # knowing what autotuning is choosing) self.description = description def benchmark(self, *args, out) -> float: # type: ignore[no-untyped-def] algo = self.to_callable() return benchmarker.benchmark(algo, args, {"out": out}) def call_name(self) -> str: raise NotImplementedError def to_callable(self): # type: ignore[no-untyped-def] raise NotImplementedError def hash_key(self) -> str: raise NotImplementedError def output_node(self) -> TensorBox: raise NotImplementedError def info_dict(self) -> dict[str, Union[PrimitiveInfoType, list[PrimitiveInfoType]]]: """Information returned here is logged to the autotune log file when that is enabled.""" return {} def autoheuristic_id(self) -> str: return "unsupported_choice" class TritonTemplateCallerBase(ChoiceCaller): def get_make_kernel_render(self) -> Any: raise NotImplementedError class MultiTemplateBuffer(TritonTemplateBuffer): """ Represents a Buffer with multiple backing implementation choices. Choices can be TritonTemplates or ExternKernels. During scheduling if there is a potential epilogue we will benchmark each of the choices with the epilogue to determine an implementation. Otherwise, the fastest base choice will be chosen. """ def __init__( self, layout: Layout, inputs: list[IRNode], choice_timings: Callable[[], dict[ChoiceCaller, float]], unfiltered_choices: list[ChoiceCaller], allowed_prologue_inps: OrderedSet[str], ) -> None: super().__init__( layout=layout, inputs=inputs, make_kernel_render=None, allowed_prologue_inps=allowed_prologue_inps, ) self._choice_timings_fn = choice_timings self._choice_timings: Optional[dict[ChoiceCaller, float]] = None self.original_inputs = inputs self._output_plannable = all( isinstance(choice, TritonTemplateCallerBase) or ( isinstance(choice, torch._inductor.select_algorithm.ExternKernelCaller) and choice.has_out_variant ) for choice in unfiltered_choices ) @property def output_plannable(self) -> bool: """ Are all possible choices TritonTemplates or Extern Kernels with out variants """ return self._output_plannable @property def choice_timings(self) -> dict[ChoiceCaller, float]: if self._choice_timings is None: self._choice_timings = self._choice_timings_fn() return self._choice_timings @contextlib.contextmanager def swap_as_triton_caller(self, caller: TritonTemplateCallerBase): # type: ignore[no-untyped-def] assert isinstance(caller, torch._inductor.select_algorithm.TritonTemplateCaller) assert self.layout == caller.layout render = self.make_kernel_render self.make_kernel_render = caller.get_make_kernel_render() try: yield finally: self.make_kernel_render = render def finalize_as_triton_caller(self, caller: TritonTemplateCallerBase) -> None: assert isinstance(caller, torch._inductor.select_algorithm.TritonTemplateCaller) assert self.get_size() == caller.layout.size assert self.get_stride() == caller.layout.stride self.make_kernel_render = caller.get_make_kernel_render() def get_min_choice(self) -> tuple[ChoiceCaller, float]: min_choice = min(self.choice_timings, key=self.choice_timings.get) # type: ignore[arg-type] return (min_choice, self.choice_timings[min_choice]) class CUDATemplateBuffer(TemplateBuffer): def __init__( # type: ignore[no-untyped-def] self, layout, inputs, make_kernel_render, workspace_size: int, template: CUDATemplate, ) -> None: super().__init__(layout, inputs, make_kernel_render) # Global memory (in bytes) needed for this template. self.workspace_size = workspace_size self.template = template def get_workspace_size(self): # type: ignore[no-untyped-def] return self.workspace_size if self.workspace_size is not None else 0 class CppTemplateBuffer(TemplateBuffer): def __init__(self, layout, inputs, make_kernel_render, template, choice) -> None: # type: ignore[no-untyped-def] super().__init__(layout, inputs, make_kernel_render) self.template = template self.choice = choice self.outputs: Optional[list[Buffer]] = None def get_layout(self) -> Layout: if isinstance(self.layout, MultiOutputLayout): assert isinstance(self.outputs, Iterable) first_output = self.outputs[0] assert isinstance(first_output, Buffer) layout = first_output.layout assert isinstance(layout, Layout) return layout else: return super().get_layout() @ir_dataclass(frozen=False) class InputsKernel(OperationBuffer): inputs: list[Buffer] def get_read_writes(self) -> dependencies.ReadWrites: reads = OrderedSet[dependencies.Dep]() StarDep = dependencies.StarDep for input in self.inputs: if isinstance(input, list): reads.update(StarDep(x.get_name()) for x in input) elif isinstance(input, ShapeAsConstantBuffer): # Skip creating dependncy for symbolics as they're visible globally continue else: reads.add(StarDep(input.get_name())) writes = OrderedSet[dependencies.Dep]( StarDep(buf.get_name()) for buf in self.get_outputs() ) return dependencies.ReadWrites( reads=reads, writes=writes, index_exprs=OrderedSet(), ) def get_reads(self) -> OrderedSet[Dep]: return self.get_read_writes().reads @classmethod def unwrap_storage_for_input(cls, x: IRNode) -> IRNode: if isinstance(x, TensorBox): x = x.data if isinstance(x, StorageBox): x = x.data if isinstance(x, BaseView) and not isinstance(x, ReinterpretView): x = ExternKernel.realize_input(x) if isinstance(x, TensorBox): # when converting to ReinterpretView fails in the # realize_input call above, the result will be wrapped # into TensorBox / StorageBox pair as a result of the # cls.copy_input call; so we should unwrap recursively return cls.unwrap_storage_for_input(x) if isinstance(x, TorchBindObject): return x assert isinstance(x, (Buffer, ReinterpretView)), x return x @staticmethod def unwrap_storage(inputs): # type: ignore[no-untyped-def] inputs_new = [] for x in inputs: if isinstance(x, list): x = [InputsKernel.unwrap_storage_for_input(i) for i in x] else: x = InputsKernel.unwrap_storage_for_input(x) inputs_new.append(x) return inputs_new def is_extern(self) -> bool: return True def num_reads(self) -> int: return 1 class NopKernel(InputsKernel): def is_no_op(self) -> bool: return True def get_reads(self) -> OrderedSet[Dep]: return OrderedSet() class ConcatKernel(NopKernel): """ There isn't actually a real kernel for concat, we just change the storage for the upstream data. """ @classmethod def create(cls, inputs, dim): # type: ignore[no-untyped-def] device = inputs[0].get_device() dtype = inputs[0].get_dtype() new_size = list(inputs[0].get_size()) offsets_start = [0] offsets_end = [new_size[dim]] assert 0 <= dim < len(new_size) for i in range(1, len(inputs)): input_size = inputs[i].get_size() offsets_start.append(new_size[dim]) assert len(input_size) == len(new_size) assert inputs[i].get_dtype() == dtype assert inputs[i].get_device() == device for j in range(len(new_size)): if j == dim: new_size[j] = new_size[j] + input_size[j] else: new_size[j] = V.graph.sizevars.guard_equals( new_size[j], input_size[j] ) offsets_end.append(new_size[dim]) output_stride = FlexibleLayout.contiguous_strides(new_size) if config.comprehensive_padding: # Ensure the output stride matches the alignment requirements output_stride = Layout._pad_strides( output_stride, new_size, inputs[0].dtype ) # If any of the inputs is in CL format, use CL format for the output for i in range(len(inputs)): x = inputs[i] if is_storage_and_layout(x): layout = x.get_layout() if isinstance( layout, FixedLayout ) and Layout.is_channels_last_contiguous(layout.size, layout.stride): # use CL stride for the output output_stride = make_channels_last_strides_for(new_size) break any_input_is_storage_and_layout = any(is_storage_and_layout(x) for x in inputs) fx_node_args = V.graph.current_node.args[0] assert isinstance(fx_node_args, list) # If any of the inputs has meta tensor and the meta tensor is in CL format, use CL format for the output if any_input_is_storage_and_layout is False and any( "val" in arg.meta and ( arg.meta["val"].is_contiguous(memory_format=torch.channels_last) or arg.meta["val"].is_contiguous(memory_format=torch.channels_last_3d) ) for arg in fx_node_args ): output_stride = make_channels_last_strides_for(new_size) concat_kernel = ConcatKernel( name=None, layout=FixedLayout( device=device, dtype=dtype, size=new_size, stride=output_stride, ), inputs=[], ) kernel = StorageBox(concat_kernel) op_names = [] for i in range(len(inputs)): input_buffer = cls.realize_into( inputs[i], SliceView.create( kernel, dim, offsets_start[i], offsets_end[i], clamp=False ), ) concat_kernel.inputs.append(input_buffer) if isinstance(inputs[i].data, BaseView): input_unwrapped = inputs[i].data.unwrap_view() else: input_unwrapped = inputs[i].data if ( input_unwrapped.is_input_buffer() and is_gpu(inputs[i].get_device().type) and not is_dynamic(input_buffer) ): op_names.append(input_buffer.get_operation_name()) if len(op_names) > 1 and V.graph.has_feature(device, BackendFeature.FOREACH): V.graph.register_operation_list(op_names) concat_kernel.name = V.graph.register_buffer(concat_kernel) concat_kernel.inputs = cls.unwrap_storage(concat_kernel.inputs) V.graph.register_operation(concat_kernel) return kernel @classmethod def can_realize_into_without_copy(cls, src, dst=None): # type: ignore[no-untyped-def] if isinstance(src, TensorBox): # unwrap a TensorBox return cls.can_realize_into_without_copy(src.data, dst) if isinstance(src.data, MultiTemplateBuffer): if ( not isinstance(src.data.layout, FixedLayout) or not src.data.output_plannable ): return False # we call can_realize_into_without_copy in cat lowering before we've decided # on output format, optimistically assume layout matches if dst is None: return True # otherwise, check equality of layouts if not len(src.get_stride()) == len(dst.get_stride()): return False return all( V.graph.sizevars.statically_known_equals(s1, s2) for s1, s2 in zip(src.get_stride(), dst.get_stride()) ) return isinstance(src.data.layout, FlexibleLayout) and not isinstance( src.data, ExternKernelAlloc ) @classmethod def realize_into(cls, src, dst): # type: ignore[no-untyped-def] # Attempt to turn this into a ReinterpretView rather than assert. # This has concessions around layout, as as_storage_and_layout # can cause us to go from flexible to fixed layout. if not isinstance(dst, ReinterpretView): if is_storage_and_layout(dst): storage, layout = as_storage_and_layout(dst) dst = ReinterpretView(data=storage, layout=layout) assert isinstance(dst, ReinterpretView), dst if isinstance(src, TensorBox): # unwrap a TensorBox return cls.realize_into(src.data, dst) if isinstance(src, StorageBox): src.realize() # ExternKernelAlloc has specific requirements for output layout, should create a copy assert hasattr(src.data, "layout") if cls.can_realize_into_without_copy(src, dst): src.data.layout = NonOwningLayout(dst) return src.data # introduce a copy pw = Pointwise.create( device=src.get_device(), dtype=src.get_dtype(), inner_fn=src.make_loader(), ranges=[ V.graph.sizevars.guard_equals(a, b) for a, b in zip(src.get_size(), dst.get_size()) ], ) return cls.realize_into(pw, dst) def should_allocate(self) -> bool: return True @ir_dataclass(frozen=False) class ExternKernel(InputsKernel): constant_args: tuple[Any, ...] = () kwargs: dict[str, Any] = dataclasses.field(default_factory=dict) output_view: Optional[ReinterpretView] = None python_kernel_name: Optional[str] = None cpp_kernel_name: Optional[str] = None # FIXME: in some cases we sill need to explicitly pass in ordered_kwargs_for_cpp_kernel # We shouldn't need to do this since the information can be retrieved from op_overload._schema. ordered_kwargs_for_cpp_kernel: Iterable[str] = dataclasses.field( default_factory=list ) op_overload: Optional[ Union[torch._ops.OpOverload, torch._ops.HigherOrderOperator] ] = None arg_properties: Optional[list[dict[str, Any]]] = None kwarg_properties: Optional[dict[str, dict[str, Any]]] = None unbacked_bindings: dict[sympy.Symbol, pytree.KeyPath] = dataclasses.field( default_factory=dict ) mutation_outputs: list[MutationOutput] = dataclasses.field(default_factory=list) def __init__( # type: ignore[no-untyped-def] self, name, layout, inputs, constant_args=(), kwargs=None, output_view=None, python_kernel_name=None, cpp_kernel_name=None, ordered_kwargs_for_cpp_kernel=(), op_overload=None, ) -> None: super().__init__( name=name, layout=layout, inputs=inputs, ) self.constant_args = constant_args self.kwargs = kwargs if kwargs else {} self.output_view = output_view self.op_overload = op_overload self.set_cpp_kernel_name(cpp_kernel_name) self.set_python_kernel_name(python_kernel_name) self.ordered_kwargs_for_cpp_kernel = ordered_kwargs_for_cpp_kernel self.collect_arg_kwarg_properties() self.unbacked_bindings = {} self.mutation_outputs = [] self.fx_node = V.graph.current_node def get_outputs(self) -> list[Buffer]: return [self, *self.mutation_outputs] def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def collect_arg_kwarg_properties(self): # type: ignore[no-untyped-def] # if self.op_overload is torch._ops.OpOverload, we can use its schema to collect additional # information for args and kwargs, e.g. type and default value, to help with the cpp wrapper codegen self.arg_properties = ( [ { "name": x.name, "type": x.real_type, "default_value": x.default_value, } for x in self.op_overload._schema.arguments if not x.kwarg_only ] if isinstance(self.op_overload, torch._ops.OpOverload) else [{} for i in range(len(self.inputs))] ) self.allarg_properties = ( { x.name: {"type": x.real_type, "default_value": x.default_value} for x in self.op_overload._schema.arguments } if isinstance(self.op_overload, torch._ops.OpOverload) else {} ) # FIXME: self.kwargs does not always match kwargs defined in schema, so sometimes # ordered_kwargs_for_cpp_kernel is explicilty passed in. if isinstance(self.op_overload, torch._ops.OpOverload): if not self.ordered_kwargs_for_cpp_kernel: self.ordered_kwargs_for_cpp_kernel = [ x.name for x in self.op_overload._schema.arguments if x.kwarg_only ] self.schema_kwargs = [ x for x in self.op_overload._schema.arguments if x.kwarg_only ] def decide_layout(self): # type: ignore[no-untyped-def] if isinstance(self.layout, FlexibleLayout): self.apply_constraint() self.freeze_layout() def codegen_comment(self, wrapper) -> None: # type: ignore[no-untyped-def] origin_str, _detailed_origin_str = get_kernel_metadata(self, wrapper) if origin_str: wrapper.writeline(origin_str) def codegen(self, wrapper): # type: ignore[no-untyped-def] raise NotImplementedError def set_cpp_kernel_name(self, cpp_kernel_name: Optional[str] = None) -> None: self.cpp_kernel_name = cpp_kernel_name if not V.graph.cpp_wrapper or not isinstance( self.op_overload, torch._ops.OpOverload ): return kernel = self.op_overload if self.cpp_kernel_name is None: # Try to construct cpp_kernel_name from op_overload if kernel.namespace == "aten": # Calling with the default kernel name can lead to ambiguous behavior like the following example. # repeat_interleave(const at::Tensor & repeats, std::optional output_size=std::nullopt) # repeat_interleave(const at::Tensor & self, int64_t repeats, # std::optional dim=std::nullopt, std::optional output_size=std::nullopt) opname = ( kernel.__name__.split(".")[0] if kernel._overloadname == "default" else kernel.__name__.replace(".", "_") ) self.cpp_kernel_name = f"at::_ops::{opname}::call" else: self.cpp_kernel_name = kernel._schema.name def set_python_kernel_name(self, python_kernel_name: Optional[str]) -> None: self.python_kernel_name = python_kernel_name if python_kernel_name is not None: return kernel = self.op_overload if kernel is None: pass elif isinstance(kernel, torch._ops.HigherOrderOperator): self.python_kernel_name = f"torch.ops.higher_order.{kernel.__name__}" else: self.python_kernel_name = ( f"{kernel.__module__.replace('._ops.', '.ops.')}.{kernel.__name__}" ) def get_kernel_name(self): # type: ignore[no-untyped-def] device = d.type if (d := self.get_device()) else V.graph.device_type return ( V.graph.wrapper_code.get_c_shim_func_name(self.cpp_kernel_name, device) # type: ignore[attr-defined] if V.graph.cpp_wrapper else self.python_kernel_name ) @staticmethod def copy_input(x): # type: ignore[no-untyped-def] pw = Pointwise.create( device=x.get_device(), dtype=x.get_dtype(), inner_fn=x.make_loader(), ranges=x.get_size(), origin_node=x.get_origin_node(), traceback=x.get_traceback(), ) pw.realize() return pw @classmethod def process_kernel( # type: ignore[no-untyped-def] cls, kernel, *args, **kwargs ) -> tuple[ Any, list[Any], list[Any], Callable[[Any, Any], Any], Optional[dict[sympy.Symbol, pytree.KeyPath]], ]: binded_args = {"args": args, "kwargs": kwargs} args_flat, args_spec = pytree.tree_flatten(binded_args) is_arg_tensor = [] # tensor_args can be either tensor or torchbind objects tensor_args = [] non_tensor_args: list[Any] = [] for arg in args_flat: is_arg_tensor.append( isinstance(arg, IRNode) and not isinstance(arg, GeneratorState) ) if is_arg_tensor[-1]: tensor_args.append(arg) else: if isinstance(arg, sympy.Expr): arg = V.graph.sizevars.shape_env.create_symintnode(arg, hint=None) non_tensor_args.append(arg) def unflatten_args(new_tensor_args, new_non_tensor_args): # type: ignore[no-untyped-def] result = [] it_tensors = iter(new_tensor_args) it_non_tensors = iter(new_non_tensor_args) for is_tensor in is_arg_tensor: if is_tensor: result.append(next(it_tensors)) else: result.append(next(it_non_tensors)) r = pytree.tree_unflatten(result, args_spec) return r.get("args", []), r.get("kwargs", {}) tensor_args = [cls.realize_input(x) for x in tensor_args] # freeze layout otherwise our output stride calculation might # become incorrect for x in tensor_args: if is_storage_and_layout(x): as_storage_and_layout(x, freeze=True) # Rerun fake tensor propagation, because Inductor may have changed the # strides of inputs and we need to determine accurately what the # output stride will be. example_args: list[ Union[torch.Tensor, torch._C.ScriptObject, torch.Generator] ] = [] # We need to retain the constant values of fake tensors that we originally # propagated the graph with, because for some operators running without a # constant would trigger an error / DataDependentException for x in tensor_args: # if x is a view of a constant, we need to realize the view # (we can't pass the constant into the kernel directly) if not isinstance(x, BaseView) and x.get_name() in V.graph.constants: example_args.append(V.graph.constants[x.get_name()]) elif ( not isinstance(x, BaseView) and x.get_name() in V.graph.torchbind_constants ): example_args.append(V.graph.torchbind_constants[x.get_name()]) elif isinstance(x, TorchBindObject): example_args.append(x.get_real_obj()) elif isinstance(x, torch._inductor.ir.GeneratorState): device_index = x.device.index assert x.device.type == "cuda" and device_index is not None example_args.append( torch.cuda.default_generators[device_index].clone_state() ) else: example_args.append(ir_node_to_tensor(x, guard_shape=True)) new_args, new_kwargs = unflatten_args(example_args, non_tensor_args) example_output = kernel(*new_args, **new_kwargs) unbacked_bindings: Optional[dict[sympy.Symbol, pytree.KeyPath]] = None if shape_env := V.fake_mode.shape_env: rebind_unbacked(shape_env, V.current_node, example_output) unbacked_bindings = compute_unbacked_bindings( shape_env, example_output, V.current_node.meta.get("val") ) example_out_li = ( [example_output] if not isinstance(example_output, (list, tuple)) else example_output ) for t in example_out_li: if isinstance(t, torch.Tensor) and t.is_sparse: msg = "sparsity not handled. Please file issue for sparse inference weights." if stack_trace := V.graph.current_node.meta.get("stack_trace", None): msg = f"{msg} Found from : \n {stack_trace}" V.graph.disable_cudagraphs_reason = msg return ( example_output, tensor_args, non_tensor_args, unflatten_args, unbacked_bindings, ) @classmethod def convert_to_reinterpret_view(cls, x): # type: ignore[no-untyped-def] """ In order to pass this to an extern kernel we need a ReinterpretView not a View. This allows us to avoid some unneeded copies. """ assert isinstance(x, BaseView) if isinstance(x, ReinterpretView): return x # NOTE: Don't use extract_read_writes here as it fails when # make_loader() inlines the computation x_unwrap_view = x.unwrap_view() buf = V.graph.get_buffer(x_unwrap_view.get_name()) assert buf is not None x_unwrap_view_fx_node = buf.get_origin_node() # Prefer channels last format according to how the format is set from eager. if ( x_unwrap_view_fx_node is not None and "val" in x_unwrap_view_fx_node.meta and isinstance(x_unwrap_view.layout, FlexibleLayout) and ( x_unwrap_view_fx_node.meta["val"].is_contiguous( memory_format=torch.channels_last ) or x_unwrap_view_fx_node.meta["val"].is_contiguous( memory_format=torch.channels_last_3d ) ) ): x_unwrap_view.freeze_layout_with_same_order( make_channels_last_strides_for(x_unwrap_view.get_size()) ) else: x_unwrap_view.freeze_layout() index_args, var_ranges = dependencies.index_vars_squeeze( x.get_size(), prefix="r", # type: ignore[arg-type] ) range_vars = index_args[0] index = x.make_indexer()(range_vars) index = V.graph.sizevars.simplify_with_ranges(index, var_ranges) strides = V.graph.sizevars.stride_vars(index, range_vars) offset = V.graph.sizevars.offset_var(index, range_vars) expected = sympy_dot(range_vars, strides) + offset if index != expected: log.debug( "convert_to_reinterpret_view failed: stride=%s offset=%s index=%s", strides, offset, index, ) raise NotImplementedError return ReinterpretView( data=x.data, layout=FixedLayout( device=x.get_device_or_error(), dtype=x.get_dtype(), size=x.get_size(), # type: ignore[arg-type] stride=strides, offset=offset, ), ) @classmethod def realize_input(cls, x): # type: ignore[no-untyped-def] if x is None: return NoneAsConstantBuffer() if isinstance(x, (sympy.Expr, sympy.logic.boolalg.Boolean, int)): return ShapeAsConstantBuffer(expr=x) if isinstance(x, Constant): return V.graph.add_tensor_constant( torch.tensor(x.value, dtype=x.get_dtype(), device=x.get_device()) ) if isinstance(x, ConstantBuffer): return x if isinstance(x, TensorBox): return cls.realize_input(x.data) if isinstance(x, ReinterpretView): return ReinterpretView( data=cls.realize_input(x.data), layout=x.get_layout() ) if isinstance(x, BaseView): x.realize() if is_storage_and_layout(x.unwrap_view()): try: return cls.convert_to_reinterpret_view(x) except NotImplementedError: pass if isinstance(x, StorageBox): # TODO(jansel): impose layout preference on realized buffer x.realize() return x if isinstance(x, (NonTensorObj, ShapeAsConstantBuffer)): return x return cls.copy_input(x) @classmethod def require_stride1(cls, x): # type: ignore[no-untyped-def] if is_storage_and_layout(x): if len(x.get_stride()) == 0: return x for stride in x.get_stride(): if stride == 1: return x return cls.copy_input(x) @classmethod def require_strides( # type: ignore[no-untyped-def] cls, x, order: Optional[Sequence[int]] = None, exact_strides: Optional[Sequence[_IntLike]] = None, allow_padding=False, ): assert order is not None or exact_strides is not None # Layout generally doesn't matter, but some consuming external ops might have requirements if x.get_numel() in (0, 1) and not exact_strides: return x # require x to have the layout if is_storage_and_layout(x): if isinstance(x.get_layout(), FlexibleLayout): if order: # If the the FlexibleLayout already has the size and stride in the required order, # freeze it to a FixedLayout by using its current size and stride. # The behavior of using its current size and stride or the given order can be different # if the size and stride has ambiguilty, for example for a 4D input where the iC = 1: # size=[s0, 1, 28, 28], stride=[784, 784, 28, 1]. If the required order is [3, 0, 2, 1] (channels last), # the current size and stride already satisfies this order. # However by freezing it to the required order, the layout will be changed to: # size=[s0, 1, 28, 28], stride=[784, 1, 28, 1]), which is not actually necessary. # fix flexiblelayout to be FixedLayout with stride_order as_storage_and_layout( x, freeze=True, want_contiguous=False, stride_order=( get_stride_order( V.graph.sizevars.size_hints(x.get_layout().stride) ) if is_stride_order_storage_and_layout(x, order) else order ), allow_padding=allow_padding, ) return x else: # If the exact_strides is given, freeze the FlexibleLayout to a FixedLayout with the exact_strides. as_storage_and_layout( x, freeze=True, want_contiguous=False, stride_order=None, allow_padding=allow_padding, exact_strides=exact_strides, ) return x elif isinstance(x.get_layout(), (FixedLayout, NonOwningLayout)) and ( (order and x.get_layout().is_stride_ordered(order)) or ( exact_strides and significant_strides_equal( exact_strides, x.get_layout().stride, x.get_size() ) ) ): return ( try_match_insignificant_strides(x, exact_strides) if exact_strides is not None else x ) elif isinstance(x.get_layout(), MutationLayoutSHOULDREMOVE): if isinstance(x.get_layout().real_layout(), FlexibleLayout): raise AssertionError( "the MutationLayoutSHOULDREMOVE's real layout shouldn't be FlexibleLayout" ) elif isinstance(x.get_layout().real_layout(), FixedLayout) and ( (order and x.get_layout().real_layout().is_stride_ordered(order)) or ( exact_strides and significant_strides_equal( exact_strides, x.get_layout().real_layout().stride, x.get_size(), ) ) ): return x # TODO - Storage to InputBuffer if isinstance(x, InputBuffer) and ( (order and x.get_layout().is_stride_ordered(order)) or ( exact_strides and significant_strides_equal( exact_strides, x.get_layout().stride, x.get_size() ) ) ): return x if ( isinstance(x, TensorBox) and isinstance(x.data, BaseView) and not isinstance(x.data, ReinterpretView) and is_storage_and_layout(x.unwrap_view()) and not isinstance(x.unwrap_view().data, ExternKernelAlloc) # type: ignore[attr-defined] ): try: x.data = cls.convert_to_reinterpret_view(x.data) if order: return cls.require_stride_order( x, order, allow_padding=allow_padding ) elif exact_strides: return cls.require_exact_strides( x, exact_strides, allow_padding=allow_padding ) except NotImplementedError: pass # Preserve ExpandView representation that would be lost during copy_input # Without representation of the expand in inductor IR, in codegen we end up # launching a grid for the full size tensor and doing redundant computation # across expanded dims. # TODO: could also be good to have a codegen fix to recognize overlapping elements expanded_dims: Optional[list[int]] = None orig_size = x.get_size() if exact_strides is not None: sizevars = V.graph.sizevars expanded_dims = [ i for i in range(len(x.get_size())) if sizevars.statically_known_equals(exact_strides[i], 0) and sizevars.statically_known_geq(x.get_size()[i], 2) ] for dim in expanded_dims: x = torch._inductor.lowering.slice_(x, dim, 0, 1) # Although this is a clone, inductor is good about fusing clones into previous # operations if they weren't realized and their layouts were flexible. x = cls.copy_input(x) as_storage_and_layout( x, freeze=True, want_contiguous=False, stride_order=order, allow_padding=allow_padding, exact_strides=exact_strides, ) if order: assert is_stride_order_storage_and_layout(x, order) elif expanded_dims: assert orig_size is not None and exact_strides is not None x = torch._inductor.lowering.expand(x, orig_size) # the expand will sometimes may change insignificant strides, so match them back return try_match_insignificant_strides(x, exact_strides) return x @classmethod def require_exact_strides(cls, x, exact_strides, allow_padding=False): # type: ignore[no-untyped-def] return cls.require_strides( x, exact_strides=exact_strides, allow_padding=allow_padding ) @classmethod def require_stride_order(cls, x, order, allow_padding=False): # type: ignore[no-untyped-def] return cls.require_strides(x, order=order, allow_padding=allow_padding) @classmethod def require_channels_last(cls, x): # type: ignore[no-untyped-def] return cls.require_stride_order(x, NHWC_STRIDE_ORDER) @classmethod def require_channels_last_3d(cls, x): # type: ignore[no-untyped-def] return cls.require_stride_order(x, NHWDC_STRIDE_ORDER) @classmethod def require_contiguous(cls, x): # type: ignore[no-untyped-def] return cls.require_stride_order(x, list(reversed(range(len(x.get_size()))))) def apply_constraint(self) -> None: pass def fill_non_provided_args(self, args, kwargs): # type: ignore[no-untyped-def] # Previously, we want to maintain forward-compatibility by skipping # default args in the serialized artifacts in fbcode. However, # some of our shim interfaces require default values being OrderedSet. # Discussed with Sherlock offline and we decided to allow serializing # default args into the C++ wrapper code for now. We will refine this # part if we see real FC requirement. More details related to FC # can be found at: # https://docs.google.com/document/d/1FzWm-sHYwmRi3x_g036kOxd99KaYquUsA-L5JwOn8ys/edit?usp=sharing assert isinstance(args, (list, tuple)) if isinstance(args, tuple): args = list(args) assert self.arg_properties, "ExternKernel.arg_properties should not be empty" n_args = len(args) n_pos_args = len(self.arg_properties) # For cpp wrapper, if some positional args are not provided, we need to check # if they're in the kwargs or use their default value if n_args < n_pos_args: log.debug( "%s has %d unprovided positional arguments. " "Will check if they are in the keyword arguments or will use default values.", self.op_overload, n_pos_args - n_args, ) for i in range(n_args, n_pos_args): arg_name = self.arg_properties[i]["name"] args.append( kwargs[arg_name] if arg_name in kwargs else self.arg_properties[i]["default_value"] ) return args def codegen_const_args(self, names: Optional[list[str]] = None): # type: ignore[no-untyped-def] if V.graph.cpp_wrapper: result = [] # Aten ops follow the convention that tensor args are before non-tensor args, # in which case the following 'len(self.inputs) + i' logic works. But this # may not be true for other ops, and if that is the case, caller needs to # pass in a list of const arg names for arg_properties lookup. name_to_arg_properties = None if names and self.arg_properties: assert len(self.constant_args) == len(names), ( "names passed to codegen_const_args does not match self.constant_args" ) name_to_arg_properties = { arg.get("name"): arg for arg in self.arg_properties } for i, x in enumerate(self.constant_args): if name_to_arg_properties is not None: prop = name_to_arg_properties.get(names[i]) # type: ignore[index] type_ = prop.get("type") if prop else None else: idx = len(self.inputs) + i type_ = ( self.arg_properties[idx].get("type") if self.arg_properties and idx < len(self.arg_properties) else None ) result.append(V.graph.wrapper_code.val_to_arg_str(x, type_)) return result else: return map(V.graph.wrapper_code.val_to_arg_str, self.constant_args) def codegen_args(self): # type: ignore[no-untyped-def] if V.graph.cpp_wrapper and self.op_overload is not None: # cpp wrapper needs special logic to fill in missing args with default values inputs = self.fill_non_provided_args( [*self.inputs, *self.constant_args], self.kwargs ) # fill_non_provided_args has handled constant args, so no need to codegen for that later need_codegen_constant_args = False else: inputs = self.inputs need_codegen_constant_args = True args = [] for i, x in enumerate(inputs): if V.graph.cpp_wrapper: assert self.arg_properties and i < len(self.arg_properties), ( "Invalid access to ExternKernel.arg_properties" ) type_ = self.arg_properties[i].get("type") args.append(V.graph.wrapper_code.val_to_arg_str(x, type_)) else: args.append(V.graph.wrapper_code.val_to_arg_str(x)) if need_codegen_constant_args: args.extend(self.codegen_const_args()) return args def get_kwargs_value(self, arg_name, **kwargs): # type: ignore[no-untyped-def] """Given an argument name, queries for values in (in order): 1. any provided kwargs for this function. 2. the class self.kwargs member. 3. any available default arguments in self.allarg_properties.""" if arg_name in kwargs: return kwargs.get(arg_name) if arg_name in self.kwargs: return self.kwargs.get(arg_name) if self.allarg_properties and arg_name in self.allarg_properties: return self.allarg_properties.get(arg_name).get("default_value") # type: ignore[union-attr] raise AssertionError(f"{arg_name} not in self.allarg_properties") def codegen_kwargs(self, skip_out=False): # type: ignore[no-untyped-def] if V.graph.cpp_wrapper: if self.op_overload is not None and len(self.schema_kwargs) == 0: # All the args should have been generated by fill_non_provided_args in codegen_args return [] kwargs = [] for arg_name in self.ordered_kwargs_for_cpp_kernel: if skip_out and arg_name == "out": # ExternKernelOut has its own logic for inserting the out parameter continue v = self.get_kwargs_value(arg_name) if isinstance(v, sympy.Expr): kwargs.append(v) else: type_ = ( self.allarg_properties.get(arg_name).get("type") # type: ignore[union-attr] if self.allarg_properties and arg_name in self.allarg_properties else None ) kwargs.append(V.graph.wrapper_code.val_to_arg_str(v, type_)) else: kwargs = [ f"{k}={V.graph.wrapper_code.val_to_arg_str(v)}" for k, v in self.kwargs.items() ] return kwargs def codegen_size_asserts(self, wrapper) -> None: # type: ignore[no-untyped-def] if config.size_asserts and not V.graph.cpp_wrapper: # comparing strides for 0 size tensor is tricky. Ignore them for now. if sympy_product(self.get_size()) == 0: return size = V.graph.wrapper_code.codegen_shape_tuple(self.get_size()) stride = V.graph.wrapper_code.codegen_shape_tuple(self.get_stride()) wrapper.writeline( f"assert_size_stride({self.get_name()}, {size}, {stride})" ) def get_group_stride(self): # type: ignore[no-untyped-def] """ get output sizes and strides, for template_codegen """ _size = self.get_size() _stride = self.get_stride() # iter_ranges = _size of output tensor, reduce_range = [] because no reduction return [_size, []], _stride def canonicalize(self): # type: ignore[no-untyped-def] """ Manually get canonicalization of the output index """ # manually generate index formula for conv sizevars = V.graph.sizevars sizes = self.get_size() strides = self.get_stride() strides = [sizevars.size_hint(x) for x in strides] # TODO: I can't tell if the symbols here are temporary index_vars = [sympy_index_symbol(f"d{i}") for i in range(len(sizes))] # reorder index vars according to stride index_order = sorted(range(len(strides)), key=strides.__getitem__, reverse=True) lookup = {pos: idx for idx, pos in enumerate(index_order)} order = [lookup[i] for i in range(len(lookup))] index_vars = [index_vars[i] for i in order] indexer = self.make_indexer() index = indexer(index_vars) new_sizes, reindex, _prune = V.graph.sizevars._simplify_loops( index_vars, sizes, [index] ) # assign new variables each dimension to deal with numbering mismatches # d0, d1, d2 could become d0, d2 -- which won't match d0, d1 _, add_var = var_builder("c") replacement = dict(zip(index_vars, reindex([add_var(x) for x in new_sizes]))) index = sympy_subs(sympy.expand(index), replacement) return index, tuple(new_sizes) def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: # NB: It's not necessary to check regular inputs as we automatically # have dependencies on them r = OrderedSet[sympy.Symbol]() for arg in self.constant_args: r |= maybe_free_unbacked_symbols(arg) for arg in self.kwargs.values(): r |= maybe_free_unbacked_symbols(arg) return r def __str__(self) -> str: kernel_name = getattr(self, "python_kernel_name", None) lines = [ f"python_kernel_name={kernel_name!r}", ] lines += [ f"{field.name}={getattr(self, field.name)}" for field in dataclasses.fields(self) ] lines.append(f"origin_node={self.origin_node!r}") return self.str_helper(lines) __repr__ = __str__ @ir_dataclass(frozen=False) class ExternKernelOut(ExternKernel): def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] self.codegen_comment(wrapper) args = [*self.codegen_args(), *self.codegen_kwargs(skip_out=True)] kernel_name = self.get_kernel_name() if ( V.graph.cpp_wrapper and self.cpp_kernel_name == "torch::inductor::_mm_plus_mm" ): # For https://github.com/pytorch/pytorch/issues/128474 kernel_name = "aoti_torch__mm_plus_mm_out" else: kernel_name = self.get_kernel_name() device = d.type if (d := self.get_device()) else V.graph.device_type wrapper.generate_extern_kernel_out( kernel_name, self.codegen_reference(), self.output_view.codegen_reference() if self.output_view else None, args, device, ) def __init__( # type: ignore[no-untyped-def] self, layout, inputs, constant_args=(), kwargs=None, output_view=None, python_kernel_name=None, cpp_kernel_name=None, ordered_kwargs_for_cpp_kernel=(), op_overload=None, ) -> None: super().__init__( None, layout, self.unwrap_storage(inputs), constant_args, kwargs or {}, None, python_kernel_name, cpp_kernel_name, ordered_kwargs_for_cpp_kernel, op_overload, ) self.name = V.graph.register_buffer(self) V.graph.register_operation(self) def should_allocate(self) -> bool: return True class RandomSeeds(ExternKernelOut): def __init__(self, count: int, device: torch.device) -> None: limits = torch.iinfo(torch.int64) super().__init__( layout=FixedLayout( device=device, dtype=torch.int64, size=[count], ), inputs=[], constant_args=[limits.min, limits.max, [count]], python_kernel_name="aten.randint.low_out", # FIXME: Ideally we should only use at::_ops::randint_low_out::call here, # but the signature is different from is at::randint_out. Again, # we can simplify the code when only keeping an ABI-compatible version. cpp_kernel_name="at::_ops::randint_low_out::call", op_overload=aten.randint.low_out, ) class ExternKernelAlloc(ExternKernel): def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] self.codegen_comment(wrapper) args = [*self.codegen_args(), *self.codegen_kwargs()] V.graph.wrapper_code.generate_extern_kernel_alloc(self, args) if isinstance(self.layout, Layout): self.codegen_size_asserts(wrapper) def __init__( # type: ignore[no-untyped-def] self, layout, inputs, constant_args=(), kwargs=None, python_kernel_name=None, cpp_kernel_name=None, ordered_kwargs_for_cpp_kernel=(), op_overload=None, ) -> None: super().__init__( None, layout, self.unwrap_storage(inputs), constant_args, kwargs or {}, None, python_kernel_name, cpp_kernel_name, ordered_kwargs_for_cpp_kernel, op_overload, ) # We need output buffers for generating kernel arguments in the # abi-compatible mode, where we retrieve outputs by pass each individual # output through the abi-compatible interface. self.outputs: Sequence[Any] = [] self.name = V.graph.register_buffer(self) V.graph.register_operation(self) def should_allocate(self) -> bool: return False def apply_constraint(self): # type: ignore[no-untyped-def] raise NotImplementedError class MutationOutput(Buffer): """ An output buffer that represents the mutation of a pre-existing buffer """ def __init__(self, layout, mutated_node, mutating_node: Operation) -> None: # type: ignore[no-untyped-def] super().__init__(name=None, layout=layout) mutated_node_name = mutated_node.get_name() V.graph.mark_buffer_mutated(mutated_node_name) self.mutation_names = [mutated_node_name] self.mutating_node: Operation = mutating_node self.name = V.graph.register_buffer(self) def get_defining_op(self) -> Operation: return self.mutating_node def get_mutation_names(self): # type: ignore[no-untyped-def] return self.mutation_names def should_allocate(self) -> bool: return False class TMADescriptor(ExternKernel): """ An IR node representing a host-side TMA descriptor in the Triton API (the ones obtained via create_{1d,2d}_tma_descriptor calls). Mostly useful for user-defined Triton kernels relying on host-side TMA; but can, in principle, be used for Inductor's Triton templates, too. """ # as TMA descriptors are immutable, # we can dedup them by the input args _CACHE: dict[Any, TMADescriptor] = {} @classmethod def create( # type: ignore[no-untyped-def] cls, tensor: IRNode, dims: list[Union[int, torch.SymInt]], block_dims: list[Union[int, torch.SymInt]], element_size: Optional[int] = None, ): key = (id(tensor), dims, block_dims, element_size) if key not in cls._CACHE: cls._CACHE[key] = TMADescriptor(tensor, dims, block_dims, element_size) return cls._CACHE[key] def __init__( self, tensor: IRNode, dims: list[Union[int, torch.SymInt]], block_dims: list[Union[int, torch.SymInt]], element_size: Optional[int] = None, ) -> None: assert len(dims) in (1, 2) assert len(dims) == len(block_dims) if element_size is None: element_size = tensor.get_dtype().itemsize self.tensor = tensor self.dims = dims self.block_dims = block_dims self.element_size = element_size self.rank = len(self.dims) inputs = [tensor] constant_args = [ *self.dims, *self.block_dims, self.element_size, ] super().__init__( None, # link back to the underlying tensor in terms of ownership # to avoid getting the underlying tensor deleted *before* # the TMADescriptor node can be deleted. NonOwningLayout( ReinterpretView( data=tensor, layout=tensor.get_layout(), ) ), inputs, tuple(constant_args), None, ) self.name = V.graph.register_buffer(self) V.graph.register_operation(self) def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] wrapper.generate_tma_descriptor(self) class UserDefinedTritonKernel(ExternKernel): def get_kernel_and_metadata(self): # type: ignore[no-untyped-def] from triton.runtime.autotuner import Autotuner from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table kernel = kernel_side_table.get_kernel(self.kernel_idx) configs = [] restore_value_args: list[str] = [] reset_to_zero_args: list[str] = [] if isinstance(kernel, Autotuner): # https://github.com/triton-lang/triton/pull/5083 # changes kernel.restore_idx to kernel.restore_value if hasattr(kernel, "restore_idx"): restore_value_args.extend( kernel.fn.arg_names[i] for i in kernel.restore_idx ) else: assert hasattr(kernel, "restore_value") restore_value_args.extend(kernel.restore_value) if hasattr(kernel, "reset_idx"): for i in kernel.reset_idx: reset_to_zero_args.append(kernel.fn.arg_names[i]) else: assert hasattr(kernel, "reset_to_zero") reset_to_zero_args.extend(kernel.reset_to_zero) configs = kernel.configs kernel = kernel.fn return kernel, configs, restore_value_args, reset_to_zero_args def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] from torch._inductor.utils import triton_version_uses_attrs_dict ( kernel, configs, restore_value_args, reset_to_zero_args, ) = self.get_kernel_and_metadata() # Definition of kernel ( new_name, triton_meta, extra_launch_args, ) = wrapper.define_user_defined_triton_kernel( kernel, configs, self.kwargs, restore_value_args, reset_to_zero_args, self.grid, ) named_args = { k: self.get_kwargs_value(k) for k in self.ordered_kwargs_for_cpp_kernel } constexpr_names = OrderedSet([kernel.arg_names[i] for i in kernel.constexprs]) args: list[Any] = [] arg_types: list[Any] = [] raw_args_filtered: list[Any] = [] for name, arg in itertools.chain( named_args.items(), zip(itertools.repeat(""), extra_launch_args) ): raw_args_filtered.append(arg) if isinstance(arg, IRNode): args.append(arg.codegen_reference()) arg_types.append(arg.get_dtype()) elif isinstance(arg, (int, float, bool, sympy.Expr)): args.append(arg) arg_types.append(type(arg)) elif name in constexpr_names: # insert a dummy value for constexpr args of unsupported type # constexprs will end up getting baked into the kernel at compile time args.append(-1) arg_types.append(int) elif arg is None: """ Filter out None args. see https://github.com/pytorch/pytorch/issues/115344 Two cases for a None arg: 1. The arg is already tl.constexpr, so leave it in 2. The arg is not tl.constexpr so we have to remove it """ if triton_version_uses_attrs_dict(): args.append(-1) arg_types.append(int) else: raw_args_filtered.pop() else: raise NotImplementedError(f"Unsupported arg type: {type(arg)}: {arg}") self.codegen_comment(wrapper) wrapper.generate_kernel_call( new_name, args, arg_types=arg_types, raw_args=raw_args_filtered, triton_meta=triton_meta, triton=True, device=self.get_device(), ) def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: # add unbacked symbols used in the grid to the ones used # in the kwargs (the latter is generated by ExternKernel) return super().get_unbacked_symbol_uses() | free_unbacked_symbols(self.grid) def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def __init__( # type: ignore[no-untyped-def] self, *, kernel_idx, grid, tma_descriptor_metadata, kernel_args ) -> None: inputs = [] kwargs = {} constant_args = [] for k, v in kernel_args.items(): if isinstance(v, TensorBox): t = InputsKernel.unwrap_storage_for_input(self.realize_input(v)) if k in tma_descriptor_metadata: t = TMADescriptor.create(t, *tma_descriptor_metadata[k]) inputs.append(t) kwargs[k] = t else: constant_args.append(v) kwargs[k] = v assert len(inputs) != 0 self.device = inputs[0].get_device() super().__init__( None, NoneLayout(device=self.device), inputs, tuple(constant_args), kwargs, ) self.kernel_idx = kernel_idx self.grid = grid kernel, configs, _, _ = self.get_kernel_and_metadata() # If we are autotuning, not all arguments will be passed self.ordered_kwargs_for_cpp_kernel = [ arg for arg in kernel.arg_names if arg in kernel_args ] from torch._higher_order_ops.triton_kernel_wrap import identify_mutated_tensors autotuned_kwargs = configs[0].kwargs if len(configs) > 0 else {} self.mutable_args = [ kernel_args[key] for key in identify_mutated_tensors( kernel, {**kernel_args, **autotuned_kwargs} ) ] self.mutation_outputs = [ MutationOutput(NoneLayout(device=self.device), buf, self) for buf in self.mutable_args ] V.graph.register_operation(self) def get_outputs(self) -> list[Buffer]: return list(self.mutation_outputs) def get_device(self) -> Optional[torch.device]: return self.device class InplaceBernoulliFallback(ExternKernel): """ This needs to be a custom class to handle mutation properly """ def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] (x,) = (t.codegen_reference() for t in self.inputs) if V.graph.cpp_wrapper: # Inductor doesn't really support aten Generator, so the Generator kwarg is always NULL here, # which needs to be explicitly generated for cpp wrapper wrapper.writeline( f"{self.get_kernel_name()}({x}, {', '.join(map(repr, self.constant_args))}, NULL){wrapper.ending}" ) else: wrapper.writeline( f"{self.get_kernel_name()}({x}, {', '.join(map(repr, self.constant_args))}){wrapper.ending}" ) def should_allocate(self) -> bool: return False def get_mutation_names(self): # type: ignore[no-untyped-def] return [self.inputs[0].get_name()] def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def __init__(self, op_overload, x, *constant_args) -> None: # type: ignore[no-untyped-def] super().__init__( None, NoneLayout(device=x.get_device()), self.unwrap_storage([x]), constant_args, op_overload=op_overload, ) V.graph.mark_buffer_mutated(x.get_name()) self.name = V.graph.register_buffer(self) V.graph.register_operation(self) # Used to deal with torch.complex types class InplaceCopyFallback(ExternKernel): """ This needs to be a custom class to handle mutation properly """ def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] (dst, src, non_blocking) = self.codegen_args() wrapper.codegen_device_copy(src, dst, non_blocking) def should_allocate(self) -> bool: return False def get_mutation_names(self): # type: ignore[no-untyped-def] return [self.inputs[0].get_name()] def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def __init__( # type: ignore[no-untyped-def] self, layout, inputs, constant_args, ) -> None: super().__init__( None, layout, inputs, constant_args, python_kernel_name="aten.copy_", cpp_kernel_name="aoti_torch_copy_", ) V.graph.mark_buffer_mutated(inputs[0].get_name()) self.name = V.graph.register_buffer(self) V.graph.register_operation(self) @classmethod def create(cls, dst, src, non_blocking: bool = False): # type: ignore[no-untyped-def] inputs = [cls.realize_input(t) for t in [dst, src]] constant_args = (non_blocking,) result = InplaceCopyFallback( NoneLayout(device=dst.get_device()), inputs, constant_args, ) return result class MutatingFirstArgExternKernel(ExternKernel): """ This needs to be a custom class to handle mutation properly """ def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] argrefs = [ *(t.codegen_reference() for t in self.inputs), *map(repr, self.constant_args), ] wrapper.writeline( f"{self.get_kernel_name()}({', '.join(argrefs)}){wrapper.ending}" ) def should_allocate(self) -> bool: return False def get_mutation_names(self): # type: ignore[no-untyped-def] return [self.inputs[0].get_name()] def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def has_side_effects(self) -> bool: return True class ResizeStorageBytes(MutatingFirstArgExternKernel): def __init__(self, variable, new_size) -> None: # type: ignore[no-untyped-def] assert isinstance(new_size, int), "TODO: dynamic shapes" super().__init__( None, NoneLayout(device=variable.get_device()), self.unwrap_storage([variable]), constant_args=(new_size,), ) V.graph.mark_buffer_mutated(variable.get_name()) self.name = V.graph.register_buffer(self) V.graph.register_operation(self) self.python_kernel_name = "inductor_ops.resize_storage_bytes_" self.cpp_kernel_name = "torch::inductor::resize_storage_bytes_" V.graph.never_reuse_buffers.add(variable.data.get_name()) class SetSourceTensorKernel(ExternKernelAlloc): def __init__(self, self_tensor, storage_tensor) -> None: # type: ignore[no-untyped-def] storage_tensor.freeze_layout() super().__init__( storage_tensor.get_layout(), [self_tensor, storage_tensor], python_kernel_name="torch.ops.aten.set_.source_Tensor", op_overload=torch.ops.aten.set_.source_Tensor, ) V.graph.never_reuse_buffers.add(self_tensor.data.get_name()) V.graph.never_reuse_buffers.add(storage_tensor.get_name()) V.graph.never_reuse_buffers.add(self.get_name()) device = storage_tensor.get_device() self.mutation_outputs = [ MutationOutput(NoneLayout(device=device), self_tensor, self), MutationOutput(NoneLayout(device=device), storage_tensor, self), ] def get_inputs_that_alias_output(self) -> Sequence[str]: return [self.inputs[0].get_name(), self.inputs[1].get_name()] class ScatterFallback(ExternKernel): """ This needs to be a custom class to handle mutation properly. This class handles both aten.scatter_ and aten.scatter_reduce_. It also handle the case `src` being a scalar properly. """ def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] reduce = self.kwargs["reduce"] if V.graph.cpp_wrapper: # Follow aten/src/ATen/native/ReductionType.h:get_operator_enum get_operator_enum = {"add": "sum", "multiply": "prod"} if reduce in get_operator_enum: reduce = get_operator_enum[reduce] if self.src_is_tensor: (x, index, src) = (t.codegen_reference() for t in self.inputs) else: (x, index) = (t.codegen_reference() for t in self.inputs) src = self.constant_args[1] wrapper.generate_scatter_fallback( x, [x, self.constant_args[0], index, src], self.cpp_kernel_name, self.python_kernel_name, self.src_is_tensor, reduce, self.codegen_kwargs(), ) def should_allocate(self) -> bool: return False def get_mutation_names(self): # type: ignore[no-untyped-def] return [self.inputs[0].get_name()] def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def __init__( # type: ignore[no-untyped-def] self, op_overload, x, dim: int, index, src, *, reduce: Optional[str] = None, include_self: bool = True, ) -> None: self.src_is_tensor = isinstance(src, TensorBox) constant_args: tuple[Any, ...] if self.src_is_tensor: tensors = [self.realize_input(t) for t in [x, index, src]] constant_args = (dim,) else: tensors = [self.realize_input(t) for t in [x, index]] constant_args = (dim, src) super().__init__( None, NoneLayout(device=x.get_device()), self.unwrap_storage(tensors), constant_args, {"reduce": reduce, "include_self": include_self}, python_kernel_name=str(op_overload), ordered_kwargs_for_cpp_kernel=["reduce", "include_self"], op_overload=op_overload, ) V.graph.mark_buffer_mutated(x.get_name()) self.name = V.graph.register_buffer(self) V.graph.register_operation(self) class IndexPutFallback(ExternKernel): """ This needs to be a custom class to handle mutation and indices properly """ def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] (x, values, *valid_indices) = (t.codegen_reference() for t in self.inputs) indices = [] iter_valid_indices = iter(valid_indices) for i, _ in enumerate(self.indices): if self.indices[i] is not None: indices.append(next(iter_valid_indices)) else: indices.append(V.graph.wrapper_code.none_str) wrapper.generate_index_put_fallback( self.get_kernel_name(), x, indices, values, *self.codegen_const_args() ) def should_allocate(self) -> bool: return False def get_mutation_names(self): # type: ignore[no-untyped-def] return [self.inputs[0].get_name()] def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: return OrderedSet() def __init__(self, op_overload, x, indices, values, accumulate) -> None: # type: ignore[no-untyped-def] self.indices = indices valid_indices = [i for i in indices if i is not None] tensors = [self.realize_input(x) for x in [x, values, *valid_indices]] cpp_kernel_name = "aoti_torch_index_put_out" super().__init__( None, NoneLayout(device=x.get_device()), self.unwrap_storage(tensors), (accumulate,), python_kernel_name="aten.index_put_", cpp_kernel_name=cpp_kernel_name, op_overload=op_overload, ) V.graph.mark_buffer_mutated(self.inputs[0].get_name()) self.name = V.graph.register_buffer(self) V.graph.register_operation(self) class DeviceCopy(ExternKernelOut): @classmethod def create(cls, x, device, non_blocking): # type: ignore[no-untyped-def] if ( not x.is_extern() and all(r in V.graph.constants for r in x.get_read_names()) and not config.aot_inductor.use_runtime_constant_folding ): return x.constant_to_device(device) V.graph.add_device_info(device) V.graph.add_device_info(x.get_device()) developer_warning("DeviceCopy in input program") constant_args = (non_blocking,) return DeviceCopy( FlexibleLayout( device=device, dtype=x.get_dtype(), size=x.get_size(), ), [cls.realize_input(x)], constant_args, ) def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] args = self.codegen_args() assert len(args) == 2 if self.output_view: wrapper.codegen_device_copy( args[0], self.output_view.codegen_reference(), args[1] ) else: wrapper.codegen_device_copy(args[0], self.codegen_reference(), args[1]) class DynamicScalar(ExternKernel): """ The result of a call to aten._local_scalar_dense. """ def get_reads(self) -> OrderedSet[Dep]: return OrderedSet() def should_allocate(self) -> bool: return False def __init__(self, sym, keypath, data) -> None: # type: ignore[no-untyped-def] data.realize() super().__init__( None, NoneLayout(device=torch.device("cpu")), self.unwrap_storage([data]) ) self.sym = sym self.keypath = keypath def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: return OrderedSet([self.sym]) def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] wrapper.codegen_dynamic_scalar(self) class AssertScalar(ExternKernel): """ The result of a call to aten._assert_scalar """ def get_reads(self) -> OrderedSet[Dep]: return OrderedSet() def should_allocate(self) -> bool: return False def __init__(self, scalar, msg) -> None: # type: ignore[no-untyped-def] super().__init__( # Buffer(name, layotu) None, NoneLayout(device=torch.device("cpu")), # InputsKernel(inputs) [], ) self.scalar = scalar self.msg = msg def has_side_effects(self) -> bool: return True def get_unbacked_symbol_uses(self): # type: ignore[no-untyped-def] return free_unbacked_symbols(self.scalar) def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] if not config.scalar_asserts: return # NB: It is EXTREMELY important not to simplify the scalar under assertion here, # because simplify is done with respect to runtime asserts. So if you have # "u0 == 0" in the runtime asserts, if you subsequently try to # simplify(u0 == 0), you will get True (because we've already runtime assert'ed # that it's true). But we're code generating the actual runtime assert here!! symbol = next(iter(self.get_unbacked_symbol_uses())) if V.graph.cpp_wrapper: symbol_str = f"std::to_string({symbol})" sizevar = V.graph.wrapper_code.codegen_cpp_sizevar( self.scalar, simplify=False ) # TODO: when we start compiling in C++20, annotate with [[unlikely]]. wrapper.writeline( f'if (!({sizevar})) {{ throw std::runtime_error("Expected {self.msg} but received " + {symbol_str}); }}' ) else: sizevar = V.graph.wrapper_code.codegen_python_sizevar( self.scalar, simplify=False ) wrapper.writeline(f"if not {sizevar}:") wrapper.writeline(f" raise RuntimeError({repr(self.msg)})") # No one should ever use this buffer, but for uniformity # define the variable and assign it None wrapper.writeline(f"{self.get_name()} = None") @ir_dataclass(frozen=False) class ExternKernelNode: name: str node: export_schema.Node class FallbackKernel(ExternKernelAlloc): def __init__( # type: ignore[no-untyped-def] self, layout, kernel, tensor_args, nontensor_args, unflatten_args, kwargs=None, *, unbacked_bindings=None, ) -> None: super().__init__( layout, tuple(tensor_args), tuple(nontensor_args), op_overload=kernel, ) self.use_runtime_dispatch = False self.unbacked_bindings = unbacked_bindings assert isinstance( kernel, ( torch._ops.OpOverload, torch._ops.HigherOrderOperator, ), ), f"Fails to create FallbackKernel for {kernel}: {type(kernel)} not supported" self.op_overload = kernel self.unflatten_args = unflatten_args self.kwargs = {} if kwargs is None else kwargs V.graph.warn_fallback(self.python_kernel_name) # type: ignore[arg-type] # args that are aliased self.alias_names: list[str] = [] # args that are mutated AND returned from the op self.mutation_names: list[str] = [] if isinstance(self.op_overload, torch._ops.HigherOrderOperator): # We assume here that HOPs with FallbackKernel are functional. # This may not always be true! HOPs must individually opt-in to # FallbackKernel, so please check this if you opt-in. return if "_c10d_functional" in self.op_overload.name(): # _c10d_functional kernels are lowered into _CollectiveKernel which # derives from FallbackKernel for the cpp codegen. The kernels # don't pass the can_auto_functionalize check, but their mutation # is handled properly by _CollectiveKernel. return schema = self.op_overload._schema # NOTE: [FallbackKernel supported operators] # We only support three types of operators: # - functional ops # - view ops # - inplace aten ops # - mutating ops that are auto-functionalizable. That is, # the operator may mutate any number of inputs, but its outputs # may not alias any of the inputs. # # The unsupported cases usually do not show up here (because # AOTAutograd functionalized them away); the only way for an in-place # op to show up here is if a lowering or pass introduced it. if torch._library.utils.mutates_and_returns_first_arg(self.op_overload): self.mutation_names.append(tensor_args[0].get_name()) return if schema.is_mutable and not can_auto_functionalize(kernel): raise NotImplementedError( f"NYI: Can't generate FallbackKernel for {kernel}" ) args, kwargs = self.unflatten_args(self.inputs, self.constant_args) def handle_aliasing_and_mutation(info, arg) -> None: # type: ignore[no-untyped-def] # Assertions to make sure we didn't mismatch args if isinstance(info.type, torch.ListType): assert isinstance(arg, (list, tuple)) if library_utils.is_tensor_like_type(info.type): # PyTorch also accepts None and scalar types for args marked as "Tensor". # We're not going to check all of them here. assert not isinstance(arg, (tuple, list)) if arg is None: return if info.alias_info is None: return def add_alias(t) -> None: # type: ignore[no-untyped-def] self.alias_names.append(t.get_name()) if info.alias_info.is_write: self.mutation_outputs.append( MutationOutput(NoneLayout(device=t.get_device()), t, self) ) if library_utils.is_tensorlist_like_type(info.type): if arg is not None: for optional_tensor_arg in arg: add_alias(optional_tensor_arg) else: assert library_utils.is_tensor_like_type(info.type) add_alias(arg) for info, arg in torch._library.utils.zip_schema(schema, args, kwargs): handle_aliasing_and_mutation(info, arg) def codegen_unbacked_symbol_defs(self, wrapper) -> None: # type: ignore[no-untyped-def] return wrapper.codegen_unbacked_symbol_defs_for_outputs( self.get_name(), self.outputs, getattr(self, "unbacked_bindings", None) ) def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: if unbacked_bindings := getattr(self, "unbacked_bindings", None): resolved = resolve_unbacked_bindings( V.graph.sizevars.shape_env, unbacked_bindings ) assert resolved is not None return resolved.keys() # type: ignore[return-value] else: return OrderedSet() def codegen_args(self): # type: ignore[no-untyped-def] @dataclasses.dataclass class Shim: ref: Any def __repr__(self) -> str: return self.ref tensor_args = [Shim(x.codegen_reference()) for x in self.inputs] args, kwargs = self.unflatten_args(tensor_args, self.constant_args) if V.graph.cpp_wrapper and isinstance(self.op_overload, torch._ops.OpOverload): args = self.fill_non_provided_args(args, kwargs) args = [ V.graph.wrapper_code.val_to_arg_str(x, param.real_type) for param, x in zip(self.op_overload._schema.arguments, args) ] else: args = [V.graph.wrapper_code.val_to_arg_str(x) for x in args] # let self.codegen_kwargs handle kwargs self.kwargs.update(kwargs) return args @staticmethod def find_device(tensor_args, example_output): # type: ignore[no-untyped-def] if tensor_args: devices = [arg.get_device() for arg in tensor_args if arg.get_device()] return devices[0] if isinstance(example_output, torch.Tensor): return example_output.device if isinstance(example_output, (list, tuple)): device_set = OrderedSet( FallbackKernel.find_device(None, x) for x in example_output ) # Remove None devices = [device for device in device_set if device] if len(devices) == 1: return devices[0] for device in devices: if is_gpu(device.type): return device return devices[0] return None def has_side_effects(self): # type: ignore[no-untyped-def] if isinstance(self.op_overload, torch._ops.HigherOrderOperator): return False return get_schema_info(self.op_overload).is_mutable() def get_inputs_that_alias_output(self): # type: ignore[no-untyped-def] return self.alias_names def get_mutation_names(self): # type: ignore[no-untyped-def] assert len(self.mutation_names) <= 1 return self.mutation_names # ProxyExecutor Design Note # We export the ExternFallbackNodes (for custom ops) into a serialized file # and run it with a host side proxy executor to address the ABI problem # This is currently only implemented for fbcode. Eventually, we will also make this work for OSS. # Detailed design doc can be found at # https://docs.google.com/document/d/1wC4DOZFaYym2t1Esz0X5yxlLI3RDnSiyRbUus3bkJ64/edit?usp=sharing def export_extern_kernel_node(self): # type: ignore[no-untyped-def] log.debug( "Extern kernel node added for node %s with target %s.", self.get_name(), self.op_overload, ) assert isinstance(self, FallbackKernel) args, kwargs = self.unflatten_args(self.inputs, self.constant_args) args = self.fill_non_provided_args(args, kwargs) ordered_kwargs = [ self.get_kwargs_value(key, **kwargs) for key in self.ordered_kwargs_for_cpp_kernel ] target = self.op_overload if not V.graph.aot_mode: # No need to serialize in the cpp wrapper JIT mode return [*args, *ordered_kwargs] serializer = GraphModuleSerializer(None, None) # type: ignore[arg-type] named_arguments = serializer.serialize_inputs(target, args, kwargs) # serialize_outputs def handle_single_output(return_type, output): # type: ignore[no-untyped-def] if isinstance(return_type, torch.TensorType): # For single Tensor out = output if isinstance(output, (list, tuple)): assert len(output) == 1 out = output[0] return export_schema.Argument.create( as_tensor=export_schema.TensorArgument(name=out.get_name()) ) elif isinstance(return_type, torch.ListType) and isinstance( return_type.getElementType(), torch.TensorType ): # For single TensorList return export_schema.Argument.create( as_tensors=[ export_schema.TensorArgument(name=out.get_name()) for out in output ] ) else: raise RuntimeError(f"Unsupported return type {type(return_type)}") if isinstance(target, torch._higher_order_ops.torchbind.CallTorchBind): returns = target.schema(args[0], args[1]).returns # type: ignore[union-attr] else: returns = target._schema.returns # type: ignore[union-attr] if len(returns) == 1: # NOTE: [special handling of all_reduce_coalesced_'s return value] # all_reduce_coalesced_ return a list of tensors via self.mutation_outputs outputs = self.outputs if self.outputs else self.mutation_outputs return_type = returns[0].real_type output_arguments = [handle_single_output(return_type, outputs)] else: # For tuple returns, e.g "-> (Tensor, Tensor)" or "-> (Tesnor, Tensor[])" # Not generating output args for self.mutation_outputs output_arguments = [ handle_single_output(return_schema.real_type, output) for return_schema, output in zip(returns, self.outputs) ] node = ExternKernelNode( name=self.get_name(), node=export_schema.Node( target=self.op_overload.name(), # type: ignore[union-attr] inputs=named_arguments, outputs=output_arguments, metadata={}, ), ) V.graph.extern_kernel_nodes.append(node) return [*args, *ordered_kwargs] def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] kernel = self.op_overload if kernel.namespace == "aten": # type: ignore[union-attr] # Aten Fallback Ops assert isinstance(kernel, torch._ops.OpOverload) if V.graph.cpp_wrapper: from torchgen.aoti.fallback_ops import inductor_fallback_ops if str(kernel) not in inductor_fallback_ops: # C shim v2 is torchgen-ed, which should cover all aten ops. # If you do hit a missed op, please update fallback_ops.py. log.warning( "%s is missing a c-shim implementation, using proxy executor as fallback", kernel, ) self.use_runtime_dispatch = True elif kernel.namespace == "_quantized": # type: ignore[union-attr] # Internal Quantized Fallback Ops assert isinstance(kernel, torch._ops.OpOverload) elif V.graph.cpp_wrapper: # For non-aten OpOverload, i.e. custom ops self.use_runtime_dispatch = True def do_runtime_dispatch() -> None: args = None exported_args = self.export_extern_kernel_node() wrapper.generate_fallback_kernel_with_runtime_lookup( self.get_name(), self.python_kernel_name, self.cpp_kernel_name, args, self.op_overload, exported_args, # NOTE: [special handling of all_reduce_coalesced_'s return value] self.outputs if self.outputs else self.mutation_outputs, ) def is_number(t: torch.JitType) -> bool: return isinstance(t, torch.NumberType) or ( isinstance(t, torch.OptionalType) and isinstance(t.getElementType(), torch.NumberType) ) self.codegen_comment(wrapper) if self.use_runtime_dispatch: do_runtime_dispatch() else: args = [*self.codegen_args(), *self.codegen_kwargs()] if ( V.graph.cpp_wrapper and isinstance(kernel, torch._ops.OpOverload) and any( "c10::complex" in arg_str and is_number(op_arg.real_type) for arg_str, op_arg in zip(args, kernel._schema.arguments) ) ): # Handle the special case where a complex number is input to a # cpp_wrapper C-shim kernel. If the corresponding argument is a number, # the torchgen-created shim API will use type "double", which cannot be # converted to from a c10::complex. In these cases, fallback to runtime # dispatch. do_runtime_dispatch() else: V.graph.wrapper_code.generate_fallback_kernel(self, args) if isinstance(self.layout, Layout): self.codegen_size_asserts(wrapper) self.codegen_unbacked_symbol_defs(wrapper) @staticmethod def tensor_to_layout(output: torch.Tensor): # type: ignore[no-untyped-def] return FixedLayout( output.device, output.dtype, convert_shape_to_inductor(output.size()), convert_shape_to_inductor(output.stride()), ) @classmethod def create(cls, kernel, *args, **kwargs): # type: ignore[no-untyped-def] fake_incorrect_kernels = (aten._fused_moving_avg_obs_fq_helper_functional,) context: AbstractContextManager[None] = ( V.graph.fake_mode if kernel not in fake_incorrect_kernels else nullcontext() # type: ignore[assignment] ) with context: ( example_output, tensor_args, non_tensor_args, unflatten_args, unbacked_bindings, ) = cls.process_kernel(kernel, *args, **kwargs) device = cls.find_device(tensor_args, example_output) if example_output is None: packed = cls( NoneLayout(device=device), kernel, tensor_args, non_tensor_args, unflatten_args, unbacked_bindings=unbacked_bindings, ) else: assert device, "Not sure where to find device info" packed = cls( MultiOutputLayout(device=device), kernel, tensor_args, non_tensor_args, unflatten_args, unbacked_bindings=unbacked_bindings, ) def generate_output(output, indices): # type: ignore[no-untyped-def] if isinstance(output, (list, tuple)): return type(output)( generate_output(output[i], indices + [(type(output), i)]) for i in range(len(output)) ) elif isinstance(output, dict): return { key: generate_output(val, indices + [(type(output), key)]) for key, val in output.items() } elif isinstance(output, torch.Tensor): return MultiOutput( cls.tensor_to_layout(output), packed, indices, ) elif isinstance(output, int): return output elif isinstance(output, torch.SymInt): return output.node.expr else: assert output is None, ( f"FallbackKernel output type {type(output)} is not supported" ) return None outputs = generate_output(example_output, []) if isinstance(outputs, (list, tuple, dict)): packed.outputs = outputs # type: ignore[assignment] else: packed.outputs = [outputs] return outputs def apply_constraint(self): # type: ignore[no-untyped-def] return super().apply_constraint() @ir_dataclass(frozen=False) class ComplexView(FallbackKernel): """View a complex number as two dtyped numbers or vice versa""" def should_allocate(self) -> bool: return False def get_inputs_that_alias_output(self) -> Sequence[str]: # Signal to codegen that our output buffer isn't safe to reuse return [self.inputs[0].get_name()] def __init__( # type: ignore[no-untyped-def] self, layout, kernel, tensor_args, nontensor_args, unflatten_args, *, unbacked_bindings=None, ) -> None: super().__init__( layout, kernel, tensor_args, nontensor_args, unflatten_args, unbacked_bindings=unbacked_bindings, ) @ir_dataclass class MultiOutputLayout(OutputSpec): device: torch.device def get_device(self) -> Optional[torch.device]: return self.device class MultiOutput(ExternKernel): # Given an input MultiOutputLayout buffer, indexes out an actual buffer # from that result. This doesn't actually produce multiple outputs, # that's MultiOutputLayout! def codegen_list_tuple_access(self, basename, indices): # type: ignore[no-untyped-def] if len(indices) > 0: itype, i = indices[0] if issubclass(itype, list): return self.codegen_list_tuple_access(f"{basename}[{i}]", indices[1:]) elif issubclass(itype, tuple): # cpp wrapper code needs to use std::get<> to access a tuple tuple_access = V.graph.wrapper_code.codegen_tuple_access( basename, self.get_name(), str(i) ) return self.codegen_list_tuple_access(tuple_access, indices[1:]) elif issubclass(itype, dict): return self.codegen_list_tuple_access(f"{basename}['{i}']", indices[1:]) else: raise AssertionError("non supported index type: ", itype) else: return basename def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] wrapper.codegen_multi_output( self.get_name(), self.codegen_list_tuple_access(self.inputs[0].get_name(), self.indices), ) self.codegen_size_asserts(wrapper) def __init__( # type: ignore[no-untyped-def] self, layout: OutputSpec, input, indices: list[tuple[Any, ...]], ) -> None: super().__init__(None, layout, [input], ()) self.name = V.graph.register_buffer(self) V.graph.register_operation(self) self.indices = indices def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: return self.inputs[0].get_unbacked_symbol_uses() def should_allocate(self) -> bool: if len(self.inputs) == 1 and ( isinstance(self.inputs[0], CppTemplateBuffer) # Grouped GEMM ): return True return False def get_inputs_that_alias_output(self) -> Sequence[str]: return [ inp.get_name() for inp in self.inputs if isinstance(inp, FallbackKernel) and len(inp.get_inputs_that_alias_output()) > 0 ] # We just use a normal dataclass for MutableBox/TensorBox/StorageBox since # they're mainly lowering-time constructs that we expect to mutate and such. @dataclasses.dataclass class MutableBox(IRNode): """ TensorBox / StorageBox allow in-place mutation of Tensors """ data: IRNode def has_exceeded_max_reads(self) -> bool: return self.data.has_exceeded_max_reads() def get_device(self) -> Optional[torch.device]: return self.data.get_device() def make_loader(self) -> Callable[[Sequence[Expr]], OpsValue]: return self.data.make_loader() def make_indexer(self) -> Callable[[Sequence[Expr]], Expr]: return self.data.make_indexer() def get_stride(self) -> Sequence[_IntLike]: return self.data.get_stride() def get_name(self) -> str: return self.data.get_name() def has_large_inner_fn(self, threshold: Optional[int] = None) -> bool: return self.data.has_large_inner_fn(threshold) def mark_reuse(self, users: int) -> None: return self.data.mark_reuse(users) def realize_hint(self) -> None: return self.data.realize_hint() def unwrap_view(self) -> IRNode: return self.data.unwrap_view() def freeze_layout(self) -> None: return self.data.freeze_layout() def freeze_layout_with_stride_order( self, order: list[int], allow_padding: bool = False ) -> None: return self.data.freeze_layout_with_stride_order(order, allow_padding) def freeze_layout_with_fill_order(self, order: list[int]) -> None: return self.data.freeze_layout_with_fill_order(order) def freeze_layout_with_same_order(self, stride: list[_IntLike]) -> None: return self.data.freeze_layout_with_same_order(stride) def freeze_layout_with_exact_strides( self, exact_strides: list[_IntLike], allow_padding: bool = False ) -> None: return self.data.freeze_layout_with_exact_strides(exact_strides, allow_padding) def get_read_writes(self) -> dependencies.ReadWrites: return self.data.get_read_writes() def get_reads(self) -> OrderedSet[Dep]: return self.data.get_reads() def num_reads(self) -> int: return self.data.num_reads() def get_storage_numel(self) -> _IntLike: return self.data.get_storage_numel() def get_reduction_type(self) -> Optional[str]: return self.data.get_reduction_type() def get_reduction_size(self) -> Sequence[sympy.Expr]: return self.data.get_reduction_size() def is_extern(self) -> bool: return self.data.is_extern() def is_no_op(self) -> bool: return self.data.is_no_op() def constant_to_device(self, device: torch.device) -> IRNode: return self.data.constant_to_device(device) def get_mutation_names(self) -> Sequence[str]: return self.data.get_mutation_names() def get_operation_name(self) -> str: return self.data.get_operation_name() def get_inputs_that_alias_output(self) -> Sequence[str]: return self.data.get_inputs_that_alias_output() def realize(self) -> Optional[str]: return self.data.realize() def get_unbacked_symbol_uses(self) -> OrderedSet[sympy.Symbol]: return self.data.get_unbacked_symbol_uses() def get_read_names(self) -> OrderedSet[str]: return self.data.get_read_names() def get_defining_op(self) -> Optional[Operation]: return self.data.get_defining_op() def codegen_reference(self, writer: Optional[IndentedBuffer] = None) -> str: return self.data.codegen_reference(writer) @property def layout(self) -> OutputSpec: # we intentionally call get_output_spec (rather than get_layout) since Buffer.layout is an OutputSpec return self.data.get_output_spec() def get_layout(self) -> Layout: return self.data.get_layout() def get_output_spec(self) -> OutputSpec: return self.data.get_output_spec() def get_size(self) -> Sequence[Expr]: return self.data.get_size() @property def dtype(self): # type: ignore[no-untyped-def] return self.data.dtype def __str__(self) -> str: if isinstance(self.data, MutableBox): line0 = f"{type(self).__name__}({type(self.data).__name__}(" endl = "))" inner = self.data.data else: line0 = f"{type(self).__name__}(" inner = self.data endl = ")" lines = [ line0, indent(str(inner)), endl, ] return "\n".join(lines) __repr__ = __str__ class TensorBox(MutableBox): @staticmethod def create(data): # type: ignore[no-untyped-def] if isinstance(data, ShapeAsConstantBuffer): return data return TensorBox(StorageBox(data)) class StorageBox(MutableBox): def is_input_buffer(self): # type: ignore[no-untyped-def] if isinstance(self.data, (InputBuffer, ReinterpretView)): return self.data.get_name() in V.graph.graph_inputs return False def is_module_buffer(self): # type: ignore[no-untyped-def] return ( isinstance(self.data, (ConstantBuffer)) and self.data.get_name() in V.graph.constants ) def realize(self) -> Optional[str]: if isinstance( self.data, ( ComputedBuffer, InputsKernel, InputBuffer, ReinterpretView, TemplateBuffer, ), ): return self.data.get_name() assert isinstance(self.data, (Pointwise, Reduction, Scan, Sort)), type( self.data ) origin_node = self.data.get_origin_node() traceback = self.data.get_traceback() self.data = ComputedBuffer( name=None, layout=FlexibleLayout( device=self.data.get_device(), dtype=self.data.get_dtype(), size=self.data.get_size(), ), data=self.data, ) self.data.name = V.graph.register_buffer(self.data) V.graph.register_operation(self.data) self.data.origins = self.origins self.data.origin_node = origin_node self.data.traceback = traceback return self.data.name def realize_hint(self) -> None: """ Called on buffers we expect to be forced to realize later. """ if ( isinstance(self.data, (Pointwise, Reduction)) and self.data.inner_fn_opcount().nontrivial_read_count > 1 ): self.realize() def has_exceeded_max_reads(self) -> bool: return isinstance(self.data, Pointwise) and ( self.num_reads() > config.realize_acc_reads_threshold or self.has_large_inner_fn() ) def should_realize_on_reuse(self, users): # type: ignore[no-untyped-def] """ A heuristic to decide if we should realize a tensor that is used multiple times. """ if users > 1 and isinstance(self.data, (Pointwise, Reduction)): if is_cpu(self.data): # Heuristic for realizing reused result of heavy ops on cpu opcount = self.data.inner_fn_opcount() heavy_ops = ["exp", "sigmoid"] # a list of heavy ops if any(x in opcount.used_ops for x in heavy_ops): return True return ( self.num_reads() > config.realize_reads_threshold or self.has_large_inner_fn() ) return False def mark_reuse(self, users: int) -> None: if self.should_realize_on_reuse(users): self.realize() def num_reads(self): # type: ignore[no-untyped-def] return self.data.num_reads() @ir_dataclass(frozen=False) class Subgraph(IRNode): name: str graph_module: torch.fx.GraphModule graph: Optional[GraphLowering] = None def _has_aliased_buffers(buffers: Sequence[IRNode]) -> bool: buffers = [ buffer.unwrap_view() if isinstance(buffer, ReinterpretView) else buffer for buffer in buffers ] # assuming the same buffer is represented by the same IRNode object return len(OrderedSet(id(buffer) for buffer in buffers)) < len(buffers) @ir_dataclass(frozen=False) class InvokeSubgraph(ExternKernel): subgraph: Optional[Subgraph] = None operands: Optional[list[TensorBox]] = None outputs: Optional[list[MultiOutput]] = None def __init__( self, subgraph: Subgraph, operands: list[TensorBox], layout: MultiOutputLayout ) -> None: super().__init__( name=None, layout=layout, inputs=operands, ) self.subgraph = subgraph self.name = V.graph.register_buffer(self) V.graph.register_operation(self) @classmethod def create(cls, subgraph: Subgraph, operands): # type: ignore[no-untyped-def] # TODO(anijain2305) - Support sym expr as operands in future. fx_operands = V.graph.current_node.args[-1] fake_operands = [x.meta["val"] for x in fx_operands] # type: ignore[union-attr] # Realize the inputs. Also intermediates can have different strides than # the inputs of the subgraph. So, force the intermediates to have same # strides as that of subgraph inputs. operands = [cls.realize_input(x) for x in operands] def handle_sym_expr(stride): # type: ignore[no-untyped-def] return [s.node.expr if isinstance(s, torch.SymInt) else s for s in stride] new_operands = [] for idx, operand in enumerate(operands): if isinstance(operand, ShapeAsConstantBuffer): new_operands.append(operand) else: example_stride = handle_sym_expr(fake_operands[idx].stride()) new_operands.append(cls.require_exact_strides(operand, example_stride)) operands = new_operands if subgraph.graph is None: # create and lower subgraphs subgraph.graph = V.graph.make_subgraph( gm=subgraph.graph_module, example_inputs=fake_operands, subgraph_name=subgraph.name, ) with V.set_graph_handler(subgraph.graph): subgraph.graph.run(*fake_operands) outputs = subgraph.graph.graph_outputs # Find the device - operands could be integers from shapes, so we can't # use operands[0] device = None for operand in operands: if not isinstance(operand, ShapeAsConstantBuffer): device = operand.get_device() break assert device is not None invoke_subgraph = InvokeSubgraph( subgraph=subgraph, operands=operands, layout=MultiOutputLayout(device=device), ) def create_output(output: IRNode, ind: int): if isinstance(output, (ShapeAsConstantBuffer, NoneAsConstantBuffer)): return output else: return MultiOutput( FixedLayout( device=output.get_device(), dtype=output.get_dtype(), size=output.get_size(), # type: ignore[arg-type] stride=output.get_stride(), offset=output.get_layout().offset, ), invoke_subgraph, [(list, ind)], ) outputs = [create_output(output, i) for i, output in enumerate(outputs)] invoke_subgraph.outputs = outputs return outputs def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] wrapper.codegen_invoke_subgraph(self) @ir_dataclass(frozen=False) class Conditional(ExternKernel): predicate: Optional[IRNode] = None operands: Optional[list[Union[TensorBox, ShapeAsConstantBuffer]]] = None true_subgraph: Optional[Subgraph] = None false_subgraph: Optional[Subgraph] = None outputs: Optional[list[MultiOutput]] = None def __init__( self, predicate: IRNode, operands: list[Union[TensorBox, ShapeAsConstantBuffer]], true_subgraph: Subgraph, false_subgraph: Subgraph, layout: MultiOutputLayout, unbacked_bindings: Optional[dict[sympy.Symbol, pytree.KeyPath]], ) -> None: self.predicate = predicate self.operands = operands self.true_subgraph = true_subgraph self.false_subgraph = false_subgraph sym_args, tensor_args = _split_by_sym_type([predicate] + operands) super().__init__( name=None, layout=layout, inputs=tensor_args, constant_args=sym_args, ) if unbacked_bindings is not None: self.unbacked_bindings = unbacked_bindings self.name = V.graph.register_buffer(self) V.graph.register_operation(self) @classmethod def create( # type: ignore[no-untyped-def] cls, predicate: TensorBox, true_fn: Subgraph, false_fn: Subgraph, operands: list[Union[TensorBox, ShapeAsConstantBuffer]], ): predicate = cls.realize_input(predicate) operands = [cls.realize_input(x) for x in operands] fx_operands = V.graph.current_node.args[-1] fake_operands = [x.meta["val"] for x in fx_operands] # type: ignore[union-attr] for subgraph in (true_fn, false_fn): if subgraph.graph is None: # create and lower subgraphs subgraph.graph = V.graph.make_subgraph( gm=subgraph.graph_module, example_inputs=fake_operands, subgraph_name=subgraph.name, ) with V.set_graph_handler(subgraph.graph): subgraph.graph.run(*fake_operands) true_outputs = true_fn.graph.graph_outputs # type: ignore[union-attr] false_outputs = false_fn.graph.graph_outputs # type: ignore[union-attr] for name, outputs in (("true_fn", true_outputs), ("false_fn", false_outputs)): if _has_aliased_buffers(true_outputs): raise AssertionError( "Output aliasing is currently not supported in compiled torch.cond. " f"The outputs of the {name} subgraph of torch.cond are aliased: {outputs}" ) # make sure true and false outputs are structurally equivalent assert len(true_outputs) == len(false_outputs), (true_outputs, false_outputs) for i, (to, fo) in enumerate(zip(true_outputs, false_outputs)): assert to.get_device() == fo.get_device(), (i, to, fo) assert to.get_dtype() == fo.get_dtype(), (i, to, fo) assert to.get_layout().offset == fo.get_layout().offset, (i, to, fo) device = next( o.get_device() for o in [predicate] + operands if not isinstance(o, ShapeAsConstantBuffer) ) unbacked_bindings = resolve_unbacked_bindings( V.graph.sizevars.shape_env, V.graph.current_node.meta.get("unbacked_bindings", None), ) assert device is not None, "cannot determine device" conditional = Conditional( predicate=predicate, operands=operands, true_subgraph=true_fn, false_subgraph=false_fn, layout=MultiOutputLayout(device=device), unbacked_bindings=unbacked_bindings, ) def _maybe_expr(s: Union[int, torch.SymInt]) -> Union[int, sympy.expr]: if isinstance(s, int): return s return s.node.expr outputs = [ MultiOutput( FixedLayout( device=output.get_device(), dtype=output.get_dtype(), size=[_maybe_expr(sz) for sz in merged_output.size()], stride=[_maybe_expr(sz) for sz in merged_output.stride()], offset=output.get_layout().offset, ), conditional, [(list, i)], ) # as the true and false outputs are equivalent, # we can use either of them here as a "template" for i, (output, merged_output) in enumerate( zip(true_outputs, V.graph.current_node.meta["val"]) ) ] conditional.outputs = outputs # type: ignore[assignment] return outputs def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] wrapper.codegen_conditional(self) wrapper.codegen_unbacked_symbol_defs_for_outputs( self.get_name(), self.outputs, getattr(self, "unbacked_bindings", {}) ) def get_unbacked_symbol_defs(self) -> OrderedSet[sympy.Symbol]: if unbacked_bindings := getattr(self, "unbacked_bindings", None): resolved = resolve_unbacked_bindings( V.graph.sizevars.shape_env, unbacked_bindings ) assert resolved is not None return resolved.keys() # type: ignore[return-value] else: return OrderedSet() def _split_by_sym_type( args: list[Any], ) -> tuple[list[ShapeAsConstantBuffer], list[Any]]: non_sym_args = [] sym_args = [] for arg in args: if isinstance(arg, ShapeAsConstantBuffer): sym_args.append(arg.expr) else: non_sym_args.append(arg) return sym_args, non_sym_args @ir_dataclass(frozen=False) class WhileLoop(ExternKernel): carried_inputs: Optional[list[Union[TensorBox, ShapeAsConstantBuffer]]] = None additional_inputs: Optional[list[Union[TensorBox, ShapeAsConstantBuffer]]] = None cond_subgraph: Optional[Subgraph] = None body_subgraph: Optional[Subgraph] = None outputs: Optional[list[MultiOutput]] = None def __init__( self, carried_inputs: list[Union[TensorBox, ShapeAsConstantBuffer]], additional_inputs: list[Union[TensorBox, ShapeAsConstantBuffer]], cond_subgraph: Subgraph, body_subgraph: Subgraph, layout: MultiOutputLayout, ) -> None: self.carried_inputs = carried_inputs self.additional_inputs = additional_inputs self.cond_subgraph = cond_subgraph self.body_subgraph = body_subgraph sym_args, tensor_args = _split_by_sym_type(carried_inputs + additional_inputs) super().__init__( name=None, layout=layout, inputs=tensor_args, constant_args=sym_args, ) self.name = V.graph.register_buffer(self) V.graph.register_operation(self) @classmethod def create( # type: ignore[no-untyped-def] cls, cond_fn: Subgraph, body_fn: Subgraph, carried_inputs: list[Union[TensorBox, ShapeAsConstantBuffer]], additional_inputs: list[Union[TensorBox, ShapeAsConstantBuffer]], ): carried_inputs = [cls.realize_input(x) for x in carried_inputs] additional_inputs = [cls.realize_input(x) for x in additional_inputs] all_inputs = carried_inputs + additional_inputs fx_all_inputs = V.graph.current_node.args[-2] + V.graph.current_node.args[-1] # type: ignore[operator] fake_all_inputs = [x.meta["val"] for x in fx_all_inputs] # type: ignore[union-attr] for subgraph in (cond_fn, body_fn): if subgraph.graph is None: # create and lower subgraphs subgraph.graph = V.graph.make_subgraph( gm=subgraph.graph_module, example_inputs=fx_all_inputs, # type: ignore[arg-type] subgraph_name=subgraph.name, ) with V.set_graph_handler(subgraph.graph): subgraph.graph.run(*fake_all_inputs) cond_outputs = cond_fn.graph.graph_outputs # type: ignore[union-attr] body_outputs = body_fn.graph.graph_outputs # type: ignore[union-attr] if _has_aliased_buffers(body_outputs): raise AssertionError( "Output aliasing is currently not supported in compiled torch.while_loop. " f"The outputs of the body_fn subgraph of torch.while_loop are aliased: {body_outputs}" ) # make sure cond_fn returns a boolean scalar Tensor assert len(cond_outputs) == 1, cond_outputs p = cond_outputs[0] if not isinstance(p, ShapeAsConstantBuffer): assert p.get_dtype() == torch.bool, p assert len(p.get_size()) == 0, p assert len(all_inputs) > 0, ( "torch.while_loop is assumed to have at least one operand." ) device = all_inputs[0].get_device() # make sure carried_inputs and body outputs are structurally equivalent assert len(carried_inputs) == len(body_outputs), (carried_inputs, body_outputs) for i, (op, bo) in enumerate(zip(carried_inputs, body_outputs)): def _guard_list_equals( lhs_exprs: list[Union[int, sympy.expr]], rhs_exprs: list[Union[int, sympy.expr]], ) -> None: for lhs, rhs in zip(lhs_exprs, rhs_exprs): V.graph.sizevars.guard_equals(lhs, rhs) _guard_list_equals(op.get_size(), bo.get_size()) _guard_list_equals(op.get_stride(), bo.get_stride()) # assume all carried_inputs and outputs are on the same device # as the MultiOutputLayout below requires single device assert op.get_device() == bo.get_device(), (i, op, bo, device) assert op.get_dtype() == bo.get_dtype(), (i, op, bo) assert op.get_layout().offset == bo.get_layout().offset, (i, op, bo) while_loop = WhileLoop( carried_inputs=carried_inputs, additional_inputs=additional_inputs, cond_subgraph=cond_fn, body_subgraph=body_fn, # asserted above that there is at least one operand layout=MultiOutputLayout(device=device), ) outputs = [ MultiOutput( FixedLayout( device=output.get_device(), dtype=output.get_dtype(), size=output.get_size(), stride=output.get_stride(), offset=output.get_layout().offset, ), while_loop, [(list, i)], ) for i, output in enumerate(body_outputs) ] for inp, out in zip(carried_inputs, outputs): if inp.get_name() in V.graph.graph_inputs: # if a carried input of the while_loop is a graph input, # it can be returned as is when the number of iterations # is zero. due to this, we can't (generally) reuse the # output buffers corresponding to the graph inputs, as # the inputs may end up being mutated. V.graph.never_reuse_buffers.add(out.get_name()) while_loop.outputs = outputs return outputs def codegen(self, wrapper) -> None: # type: ignore[no-untyped-def] wrapper.codegen_while_loop(self) class EffectfulKernel(FallbackKernel): def __init__( # type: ignore[no-untyped-def] self, layout, kernel, tensor_args, nontensor_args, unflatten_args, kwargs=None, *, unbacked_bindings=None, ) -> None: super().__init__( layout, kernel, tensor_args, nontensor_args, unflatten_args, kwargs=None, unbacked_bindings=unbacked_bindings, ) from torch._higher_order_ops.effects import get_effect_key uncovered_args = [ a.value if isinstance(a, TorchBindObject) else a for a in tensor_args ] effect_type = get_effect_key(kernel, (*nontensor_args, *uncovered_args), kwargs) assert effect_type is not None self.effect_type = effect_type self.prev_effect_buffer = V.graph.effectful_ops.get(effect_type, None) V.graph.effectful_ops[effect_type] = self def get_read_writes(self) -> dependencies.ReadWrites: read_writes = super().get_read_writes() if self.prev_effect_buffer is not None: read_writes.reads.add( dependencies.StarDep(self.prev_effect_buffer.get_name()) ) return read_writes def has_side_effects(self) -> bool: return True class NonTensorObj(IRNode): pass @ir_dataclass class TorchBindObject(NonTensorObj): from torch._library.fake_class_registry import FakeScriptObject name: str value: Union[FakeScriptObject, torch.ScriptObject] def get_name(self): # type: ignore[no-untyped-def] return self.name def codegen_reference(self, writer: Optional[IndentedBuffer] = None) -> str: return self.name def get_value(self) -> Union[FakeScriptObject, torch.ScriptObject]: return self.value def get_real_obj(self) -> torch.ScriptObject: if isinstance(self.value, torch.ScriptObject): return self.value else: return self.value.real_obj def get_buf_bytes(self) -> int: # Returns the sum of all tensors in the flattened object real_script_obj = self.get_real_obj() flat_dict = dict(real_script_obj.__obj_flatten__()) # type: ignore[attr-defined] flat_elems = pytree.tree_flatten(flat_dict)[0] flat_sizes = [ x.element_size() * x.numel() for x in flat_elems if isinstance(x, torch.Tensor) ] return functools.reduce(lambda x, y: x + y, flat_sizes, 0) @ir_dataclass class GeneratorState(NonTensorObj): name: str device: torch.device def get_name(self): # type: ignore[no-untyped-def] return self.name def codegen_reference(self, writer: Optional[IndentedBuffer] = None) -> str: return self.name class _CollectiveKernel(FallbackKernel): def should_allocate(self) -> bool: return False def has_side_effects(self) -> bool: return True # This is identical to FallbackKernel.set_cpp_kernel(), minus the # part that checks against input aliasing and mutation. def set_cpp_kernel_name(self, cpp_kernel_name: Optional[str] = None) -> None: assert type(self.op_overload) is torch._ops.OpOverload, ( "Setting cpp kernel needs a valid op_overload" ) kernel = self.op_overload self.cpp_kernel_name = kernel._schema.name self.ordered_kwargs_for_cpp_kernel = [ x.name for x in kernel._schema.arguments if x.kwarg_only ] # NOTE: [In-Place Collective Safety] # Between the initiation and completion of an in-place collective, the # input buffers are subject to both volatile reads and volatile writes. # They must not be read, written to or reused by another kernel. To ensure # the constraints, we model collective -> wait_tensor as as two-step # mutation of the input buffers. @classmethod def create_inplace( # type: ignore[no-untyped-def] cls, kernel, inputs: Union[TensorBox, list[TensorBox]], *args, **kwargs ) -> None: with V.graph.fake_mode: ( _example_output, tensor_args, non_tensor_args, unflatten_args, unbacked_bindings, ) = cls.process_kernel(kernel, inputs, *args, **kwargs) assert not unbacked_bindings, f"{kernel} {unbacked_bindings}" for tensor_arg in tensor_args: tensor_arg.realize() device = tensor_args[0].get_device() packed = cls( NoneLayout(device=device), kernel, tensor_args, non_tensor_args, unflatten_args, ) inps = pytree.tree_leaves(inputs) packed.mutation_outputs.extend( [MutationOutput(NoneLayout(device=device), buf, packed) for buf in inps] ) # For inplace collective ops, the input is guaranteed to be alias of the returned value of op. packed.alias_names.extend([inp.get_name() for inp in inps]) if "out" in kwargs: packed.mutation_outputs.append( MutationOutput(NoneLayout(device=device), kwargs["out"], packed) ) # For out-variant collective ops, the `out=` arg is guaranteed to be alias of the returned value of op. packed.alias_names.append(kwargs["out"].get_name()) # NOTE: [Out-of-Place Collective Safety] # Between the initiation and completion of an out-of-place collective: # # Input buffers: # - Are subject to volatile reads # - Can be read by another kernel # - Must not be written to or reused by another kernel # # Output buffers: # - Are subject to volatile writes # - Must not be read, written to or reused by another kernel # # To ensure the safety of input buffers without sacrificing read # availability, we add input buffers as read deps of wait_tensor kernels. # # To ensure the safety of output buffers, we model wait_tensor as a # mutation to the output buffer. Note we also assumes the user program being # correct and the output buffer is not consumed by kernels other than # wait_tensor. # # TODO(yifu): add a pre-grad pass to validate the correctness of collective # usage in the user program. @classmethod def create_out_of_place( # type: ignore[no-untyped-def] cls, kernel, inputs: Union[TensorBox, list[TensorBox]], *args, **kwargs ): with V.graph.fake_mode: ( example_output, tensor_args, non_tensor_args, unflatten_args, unbacked_bindings, ) = cls.process_kernel(kernel, inputs, *args, **kwargs) assert not unbacked_bindings, f"{kernel}, {unbacked_bindings}" for tensor_arg in tensor_args: tensor_arg.realize() if isinstance(example_output, list): device = cls.find_device(tensor_args, example_output) packed = cls( MultiOutputLayout(device=device), kernel, tensor_args, non_tensor_args, unflatten_args, ) packed.outputs = [ MultiOutput( cls.tensor_to_layout(tensor), packed, [(list, i)], ) for i, tensor in enumerate(example_output) ] return packed.outputs else: packed = cls( cls.tensor_to_layout(example_output), kernel, tensor_args, non_tensor_args, unflatten_args, ) packed.outputs = [packed] return packed class _WaitKernel(_CollectiveKernel): def get_volatile_reads(self): # type: ignore[no-untyped-def] inp = self.inputs[0] if isinstance(inp, _CollectiveKernel): # Out-of-place single-output return [inp.inputs[0]] elif isinstance(inp, MultiOutput): # This can be two things: # 1. Out-of-place multi-output coll # 2. In-place coll with inputs coming from another MultiOutput coll = inp.inputs[0] # Case 1 if isinstance(coll, _CollectiveKernel): _, idx = inp.indices[0] return [coll.inputs[idx]] # Case 2 return [] else: # In-place requires no additional deps handling for volatile # reads since the inputs are mutated. return [] @classmethod def create_wait(cls, kernel, inp: TensorBox) -> None: # type: ignore[no-untyped-def] with V.graph.fake_mode: ( _example_output, tensor_args, non_tensor_args, unflatten_args, unbacked_bindings, ) = cls.process_kernel(kernel, inp) assert not unbacked_bindings, f"{kernel} {unbacked_bindings}" packed = cls( NoneLayout(device=inp.get_device()), kernel, tensor_args, non_tensor_args, unflatten_args, ) packed.mutation_outputs.append( MutationOutput(NoneLayout(device=inp.get_device()), inp, packed) ) def get_read_writes(self) -> dependencies.ReadWrites: read_writes = super().get_read_writes() # See [Out-of-Place Collective Safety]. volatile_reads = self.get_volatile_reads() for vr in volatile_reads: read_writes.reads.add(dependencies.StarDep(vr.get_name())) return read_writes # NB: recursive structure here reflects val_to_arg_str, avoid # calling free_unbacked_symbols on "exotic" types that don't get pexpr # treatment def maybe_free_unbacked_symbols(s: object) -> OrderedSet[Symbol]: if isinstance(s, (SymTypes, Expr)): # This branch should be impossible in return position return free_unbacked_symbols(s) elif isinstance(s, (tuple, list)): r = OrderedSet[sympy.Symbol]() for t in s: r |= maybe_free_unbacked_symbols(t) return r elif isinstance(s, torch.Tensor): # This branch is impossible in constant-args position return free_unbacked_symbols(s) else: return OrderedSet()