mirror of
https://github.com/pytorch/pytorch.git
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da67a6a7bb
Uses the set_linter from https://github.com/pytorch/pytorch/pull/138454 and considerable manual editing Pull Request resolved: https://github.com/pytorch/pytorch/pull/138466 Approved by: https://github.com/eellison
253 lines
8.8 KiB
Python
253 lines
8.8 KiB
Python
# mypy: allow-untyped-defs
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import operator
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from collections import defaultdict
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from typing import Any, Callable, DefaultDict, Dict, Optional, Tuple, Type
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import sympy
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import torch
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import torch.fx
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from torch._dispatch.python import enable_python_dispatcher
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from torch.fx.experimental.symbolic_shapes import (
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compute_unbacked_bindings,
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rebind_unbacked,
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statically_known_true,
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sym_eq,
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)
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from torch.utils import _pytree as pytree
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from torch.utils._ordered_set import OrderedSet
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from torch.utils._pytree import tree_map
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from .virtualized import V
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# Check the pattern: (nn.module, F.function/torch.Tensor.method) matched.
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# Works for length 2 patterns with 1 module and 1 function/method.
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def matches_module_function_pattern(
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pattern: Tuple[Type[torch.nn.modules.Module], Callable[..., Any]],
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node: torch.fx.node.Node,
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modules: Dict[str, torch.nn.modules.Module],
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) -> bool:
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if len(node.args) == 0:
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return False
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if not isinstance(node.args[0], torch.fx.Node) or not isinstance(
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node, torch.fx.Node
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):
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return False
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# the first node is call_module
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if node.args[0].op != "call_module":
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return False
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if not isinstance(node.args[0].target, str):
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return False
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if node.args[0].target not in modules:
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return False
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if type(modules[node.args[0].target]) is not pattern[0]:
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return False
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# the second node is call_function or call_method
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if node.op != "call_function" and node.op != "call_method":
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return False
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if node.target != pattern[1]:
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return False
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# make sure node.args[0] output is only used by current node.
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if len(node.args[0].users) > 1:
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return False
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return True
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class FakeTensorUpdater:
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"""
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The main idea here is that it's difficult to maintain accurate fake
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tensors (our primary form of metadata) for each node in our graph as we
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transform it.
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The most reliable way to obtain this information is by rerunning
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faketensor propagation. However, in general, faketensor propagation is
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fairly expensive. So, instead we'd like to only rerun faketensor
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propagation on nodes that have changed.
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In order to detect which nodes have changed, we first hash its node,
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target, and argument lists (which are immutable in FX).
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Then, whenever we call incremental_update, we check which FX nodes have a
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new hash, and recompute the faketensor metadata for that node. Then, we
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continue to recursively compute the faketensors for all users until the
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fake tensors stop changing.
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"""
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def __init__(self, graph: torch.fx.Graph) -> None:
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self.processed_hashes = OrderedSet[Any]()
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self.graph = graph
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for node in self.graph.nodes:
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self.processed_hashes.add(self.hash_node(node))
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def hash_node(self, node: torch.fx.Node):
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# todo(chilli): Not a great hash function
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return (node, node.target, id(node.args), id(node.kwargs))
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def incremental_update(self):
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existing_storages: DefaultDict[Optional[int], int] = defaultdict(int)
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for node in self.graph.nodes:
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existing_storages[get_node_storage(node)] += 1
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def is_intlist_same(new, old):
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return statically_known_true(sym_eq(new, old))
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def is_fake_tensor_same(new, old):
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if type(new) != type(old):
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return False
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if isinstance(new, (list, tuple)):
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if len(new) != len(old):
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return False
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return all(
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is_fake_tensor_same(new_i, old_i) for new_i, old_i in zip(new, old)
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)
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if new is None:
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return old is None
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if not isinstance(new, torch.Tensor):
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assert isinstance(
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new, (torch.SymInt, torch.SymBool, torch.SymFloat)
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), f"Unknown type {type(new)} in {self.graph}"
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return (
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new.node.shape_env._maybe_evaluate_static(
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sympy.Eq(new.node.expr, old.node.expr)
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)
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== sympy.true
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)
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if not is_intlist_same(new.shape, old.shape) or new.layout != old.layout:
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return False
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if new.layout == torch.strided and (
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not is_intlist_same(new.stride(), old.stride())
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or not statically_known_true(
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new.storage_offset() == old.storage_offset()
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)
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):
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return False
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if new.device != old.device:
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return False
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if get_storage(new) == get_storage(old):
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return True
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# This is the case where it returns a completely fresh storage that's used nowhere else.
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if (
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existing_storages[get_storage(old)] == 1
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and get_storage(new) not in existing_storages
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):
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return True
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return False
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def should_process_node(node):
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# node.target for nodes returning true from this function
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# are called under fake mode and does not work for inductor
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# lowerings. We check if the node.target is an aten operator
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# or operator.getitem which is used when returning multiple
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# tensors from an op.
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return node.op == "call_function" and (
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isinstance(node.target, torch._ops.OpOverload)
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or node.target == operator.getitem
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)
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to_process = OrderedSet[int]()
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for node in self.graph.nodes:
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if (
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self.hash_node(node) in self.processed_hashes
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and id(node) not in to_process
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):
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continue
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if not should_process_node(node):
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continue
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is_valid, args, kwargs = get_fake_args_kwargs(node)
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if not is_valid:
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continue
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with V.fake_mode, enable_python_dispatcher():
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new_fake_tensor = node.target(*args, **kwargs)
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if "val" in node.meta and is_fake_tensor_same(
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new_fake_tensor, node.meta["val"]
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):
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continue
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rebind_unbacked(V.fake_mode.shape_env, node, new_fake_tensor)
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node.meta["val"] = new_fake_tensor
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if (shape_env := V.fake_mode.shape_env) and (
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symbol_to_path := compute_unbacked_bindings(shape_env, new_fake_tensor)
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):
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# Refresh the bindings to the new symbols
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node.meta["unbacked_bindings"] = symbol_to_path
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existing_storages[get_node_storage(node)] += 1
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to_process.update([id(user) for user in node.users])
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self.processed_hashes.add(self.hash_node(node))
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def get_storage(t: torch.Tensor) -> int:
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return t.untyped_storage()._cdata
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def get_node_storage(node: torch.fx.Node) -> Optional[int]:
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if "val" not in node.meta:
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return None
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if not isinstance(node.meta["val"], torch.Tensor):
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return None
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if not torch._C._has_storage(node.meta["val"]):
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return None
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return get_storage(node.meta["val"])
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def get_fake(x):
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if isinstance(x, torch.fx.Node):
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if "val" not in x.meta:
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return x
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return x.meta["val"]
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return x
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def get_fake_args_kwargs(x: torch.fx.Node) -> Tuple[bool, Tuple[Any], Dict[str, Any]]:
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"""
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First value returns a boolean if any of the input nodes don't have a faketensor.
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"""
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args, kwargs = tree_map(get_fake, (x.args, x.kwargs))
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if any(
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isinstance(a, torch.fx.Node) for a in pytree.arg_tree_leaves(*args, **kwargs)
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):
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return False, args, kwargs
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return True, args, kwargs
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def is_node_realized(node: torch.fx.Node) -> bool:
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"""Returns true if a node is always realized when lowered to inductor IR.
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NOTE: This may return some false negatives. e.g. it doesn't
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handle buffers realized heuristically during lowering, or
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buffers realized indirectly through view ops.
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"""
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from torch._inductor.lowering import fallbacks, needs_realized_inputs
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def is_buffer(node: torch.fx.Node) -> bool:
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if node.op == "call_function" and node.target is operator.getitem:
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# For nodes with multiple outputs, we get the fx graph:
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# foo = torch.ops.aten.foo(...)
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# getitem = foo[0]
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# getitem_1 = foo[1]
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# where we need to check if foo is a fallback kernel
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return is_buffer(node.args[0]) # type: ignore[arg-type]
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return node.op in ("placeholder", "output") or node.target in fallbacks
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if is_buffer(node):
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return True
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def realizes_inputs(node: torch.fx.Node) -> bool:
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return node.op == "output" or node.target in needs_realized_inputs
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if any(realizes_inputs(user) for user in node.users):
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return True
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# Otherwise, assume node isn't realized
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return False
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