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5472923998
With the current `Dim`-based dynamic shapes API for export, one can express that shapes of different input shapes must be equal by reusing the same `Dim`. However, non-trivial relationships between such input shapes cannot be expressed. Recently we are seeing more and more examples of code that require this additional expressibility, e.g., where a pair of shapes might differ by one, or a shape might be double another (or simply even). This PR introduces the concept of a "derived" `Dim`, i.e., a linear arithmetic expression over a `Dim`. By using a combination of `Dim`s and derived `Dim`s to specify input shapes, the desired relationships can be expressed naturally. E.g., a pair of shapes might be `dim` and `dim + 1`, or `dim` and `2*dim`, or even `2*dim` and `dim + 1`. We extend the current infrastructure that translates `Dim`s to deprecated `dynamic_dim`-based constraints to work with derived `Dim`s. As usual, we raise constraint violation errors when shape guards cannot be verified given a dynamic shapes spec; suggest fixes; and raise runtime errors when future inputs violate the spec. Importantly, some guards that used to cause forced specializations in the constraint solver because they were deemed "too complex" now do not do so, because they can now be specified as constraints. Since this was what motivated the introduction of a `disable_constraint_solver` flag to some internal APIs, we may not need that flag any more. Note that shapes of placeholders in exported programs can now contain symbolic expressions and not just symbols. Differential Revision: D53254587 Pull Request resolved: https://github.com/pytorch/pytorch/pull/118729 Approved by: https://github.com/ezyang
399 lines
15 KiB
Python
399 lines
15 KiB
Python
import dataclasses
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import math
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import operator
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from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
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import torch
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from torch._subclasses.fake_tensor import FakeTensor
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from torch.export import ExportedProgram
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from torch.utils._pytree import (
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_register_pytree_node,
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Context,
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FlattenFunc,
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FromDumpableContextFn,
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KeyPath,
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keystr,
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MappingKey,
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SequenceKey,
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ToDumpableContextFn,
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UnflattenFunc,
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)
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def _check_input_constraints_for_graph(
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input_placeholders: List[torch.fx.Node], flat_args_with_path, range_constraints
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):
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def get_keystr(key_path: KeyPath) -> str:
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"""For a given index into the flat_args, return a human readable string
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describing how to access it, e.g. "*args["foo"][0].bar"
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"""
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# Prefix the keypath with "*args" or "**kwargs" to make it clearer where
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# the arguments come from. Ultimately we ought to serialize the
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# original arg names for the best error message here.
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args_kwargs_key_path = key_path[0]
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assert isinstance(args_kwargs_key_path, SequenceKey)
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if args_kwargs_key_path.idx == 0:
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return f"*args{keystr(key_path[1:])}"
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else:
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kwarg_key = key_path[1]
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assert isinstance(kwarg_key, MappingKey)
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name = str(kwarg_key)[1:-1] # get rid of the enclosed []
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return f"{name}{keystr(key_path[2:])}"
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import sympy
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from torch._export.passes.add_runtime_assertions_for_constraints_pass import (
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_convert_range_to_int,
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)
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from torch.utils._sympy.solve import try_solve
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if len(flat_args_with_path) != len(input_placeholders):
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raise RuntimeError(
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"Unexpected number of inputs "
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f"(expected {len(input_placeholders)}, got {len(flat_args_with_path)})"
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)
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# NOTE: export already guarantees that the same symbol is used in metadata
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# for all InputDims related by equality constraints, so we can just unify
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# symbols with given input dimension values to check equality constraints.
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unification_map: "Dict[sympy.Symbol, Any]" = {}
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for (key_path, arg), node in zip(flat_args_with_path, input_placeholders):
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node_val = node.meta.get("val")
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if isinstance(node_val, FakeTensor):
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if not isinstance(arg, torch.Tensor):
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)} to be a tensor, but got {type(arg)}",
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)
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if len(node_val.shape) != len(arg.shape):
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raise RuntimeError(
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f"Unexpected number of dimensions in input at {get_keystr(key_path)}.shape "
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f"(expected {node_val.shape}, got {arg.shape})"
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)
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for j, (arg_dim, node_dim) in enumerate(zip(arg.shape, node_val.shape)):
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# TODO(avik): Assert the following property in the IR verifier:
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# node_dim is either an int or a SymInt containing an int or a unary sympy.Expr
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if (
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isinstance(node_dim, torch.SymInt)
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and len(node_dim.node.expr.free_symbols) == 1
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):
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symbol = next(iter(node_dim.node.expr.free_symbols))
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if symbol in unification_map:
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existing_dim = node_dim.node.expr.subs(unification_map)
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if arg_dim != existing_dim:
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)}.shape[{j}] to be equal to "
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f"{existing_dim}, but got {arg_dim}",
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)
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else:
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if isinstance(arg_dim, torch.SymInt) and isinstance(
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node_dim.node.expr, sympy.Symbol
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):
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# this can happen when, say, arg is a fake tensor
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unification_map[symbol] = arg_dim
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else:
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solution = try_solve(
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sympy.Eq(node_dim.node.expr, arg_dim), symbol
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)
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if solution is None:
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raise RuntimeError( # noqa: TRY200
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f"Expected input {node.name}.shape[{j}] = {arg_dim} to be "
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f"of the form {node_dim.node.expr}, where {symbol} is an integer"
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)
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else:
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unification_map[symbol] = int(solution[1])
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if node_dim.node.expr in range_constraints:
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min_val, max_val = _convert_range_to_int(
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range_constraints[node_dim.node.expr]
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)
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# NOTE: we allow dimensions to be 0/1 at runtime
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if min_val > 2:
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if arg_dim < min_val:
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)}.shape[{j}] to be >= "
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f"{min_val}, but got {arg_dim}",
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)
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if max_val < math.inf:
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if arg_dim > max_val:
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)}.shape[{j}] to be <= "
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f"{max_val}, but got {arg_dim}",
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)
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else:
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if arg_dim != node_dim:
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)}.shape[{j}] to be equal to "
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f"{node_dim}, but got {arg_dim}",
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)
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elif isinstance(node_val, (int, float, str)):
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if type(arg) != type(node_val) or arg != node_val:
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raise RuntimeError(
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f"Expected input at {get_keystr(key_path)} to be equal to {node_val}, but got {arg}",
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)
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def register_dataclass_as_pytree_node(
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cls: Type[Any],
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flatten_fn: Optional[FlattenFunc] = None,
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unflatten_fn: Optional[UnflattenFunc] = None,
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*,
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serialized_type_name: Optional[str] = None,
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to_dumpable_context: Optional[ToDumpableContextFn] = None,
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from_dumpable_context: Optional[FromDumpableContextFn] = None,
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return_none_fields: bool = False,
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) -> None:
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assert dataclasses.is_dataclass(
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cls
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), f"Only dataclasses can be registered with this function: {cls}"
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def default_flatten_fn(obj: Any) -> Tuple[List[Any], Context]:
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flattened = []
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flat_names = []
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none_names = []
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for f in dataclasses.fields(obj):
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name, val = f.name, getattr(obj, f.name)
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if val is not None or return_none_fields:
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flattened.append(val)
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flat_names.append(name)
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else:
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none_names.append(name)
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return flattened, [flat_names, none_names]
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def default_unflatten_fn(values: Iterable[Any], context: Context) -> Any:
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flat_names, none_names = context
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return cls(**dict(zip(flat_names, values)), **dict.fromkeys(none_names))
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flatten_fn = flatten_fn if flatten_fn is not None else default_flatten_fn
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unflatten_fn = unflatten_fn if unflatten_fn is not None else default_unflatten_fn
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if (to_dumpable_context is None) ^ (from_dumpable_context is None):
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raise ValueError(
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f"Both to_dumpable_context and from_dumpable_context for {cls} must "
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"be None or registered."
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)
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_register_pytree_node(
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cls,
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flatten_fn,
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unflatten_fn,
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serialized_type_name=serialized_type_name,
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to_dumpable_context=to_dumpable_context,
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from_dumpable_context=from_dumpable_context,
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)
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def is_param(program: ExportedProgram, node: torch.fx.Node) -> bool:
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"""
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Checks if the given node is a parameter within the exported program
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"""
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return node.name in program.graph_signature.inputs_to_parameters
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def get_param(
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program: ExportedProgram,
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node: torch.fx.Node,
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) -> Optional[torch.nn.Parameter]:
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"""
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Returns the parameter associated with the given node in the exported program.
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Returns None if the node is not a parameter within the exported program
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"""
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if is_param(program, node):
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parameter_name = program.graph_signature.inputs_to_parameters[node.name]
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return program.state_dict[parameter_name]
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return None
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def is_buffer(program: ExportedProgram, node: torch.fx.Node) -> bool:
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"""
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Checks if the given node is a buffer within the exported program
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"""
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return node.name in program.graph_signature.inputs_to_buffers
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def get_buffer(
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program: ExportedProgram,
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node: torch.fx.Node,
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) -> Optional[torch.Tensor]:
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"""
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Returns the buffer associated with the given node in the exported program.
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Returns None if the node is not a buffer within the exported program
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"""
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if is_buffer(program, node):
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buffer_name = program.graph_signature.inputs_to_buffers[node.name]
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if buffer_name in program.graph_signature.non_persistent_buffers:
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return program.constants[buffer_name]
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else:
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return program.state_dict[buffer_name]
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return None
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def is_lifted_tensor_constant(
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program: ExportedProgram,
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node: torch.fx.Node,
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) -> bool:
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"""
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Checks if the given node is a lifted tensor constant within the exported program
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"""
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return node.name in program.graph_signature.inputs_to_lifted_tensor_constants
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def get_lifted_tensor_constant(
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program: ExportedProgram,
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node: torch.fx.Node,
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) -> Optional[torch.Tensor]:
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"""
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Returns the lifted tensor constant associated with the given node in the exported program.
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Returns None if the node is not a lifted tensor constant within the exported program
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"""
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if is_lifted_tensor_constant(program, node):
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lifted_tensor_name = program.graph_signature.inputs_to_lifted_tensor_constants[
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node.name
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]
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return program.constants[lifted_tensor_name]
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return None
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def sequential_split(gm: torch.fx.GraphModule, node_call_back) -> torch.fx.GraphModule:
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"""
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Splits the graph module into multiple submodules based on the node_call_back.
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The node_call_back should return True if the node is a delimiter. Delimiter will be
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the first node in the next submodule.
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"""
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from torch.fx.passes.split_module import split_module
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split_map = {}
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split_id = 0
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for node in gm.graph.nodes:
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if node_call_back(node):
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split_id += 1
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split_map[node] = split_id
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new_gm = split_module(
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gm,
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gm,
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lambda node: split_map[node],
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keep_original_order=True,
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keep_original_node_name=True,
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)
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# Keep the codegen from original graph module to preserve e.g. pytree info.
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new_gm.graph._codegen = gm.graph._codegen
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new_gm.recompile()
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return new_gm
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def nodes_filter(nodes: List[torch.fx.Node], node_call_back) -> List[torch.fx.Node]:
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"""Returns the nodes that match the node_call_back as a list."""
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return [node for node in nodes if node_call_back(node)]
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def nodes_first(
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nodes: List[torch.fx.Node], node_call_back=None
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) -> Optional[torch.fx.Node]:
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"""
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Returns the first node that matches the node_call_back. If no node matches, returns None.
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When node_call_back is None, returns the first node in the node list.
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"""
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ret = nodes_filter(nodes, node_call_back if node_call_back else lambda node: True)
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if len(ret) > 0:
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return ret[0]
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return None
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def nodes_count(nodes: List[torch.fx.Node], node_call_back) -> int:
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"""Returns the number of nodes that match the node_call_back."""
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return len(nodes_filter(nodes, node_call_back))
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def nodes_map(nodes: List[torch.fx.Node], node_call_back) -> List[torch.fx.Node]:
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"""
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Sequentially visit the nodes list and invoke node_call_back on each element.
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Returns the nodes list after the node_call_back is invoked on each element.
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"""
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for node in nodes:
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node_call_back(node)
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return nodes
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def node_replace_(
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old_node: torch.fx.Node, new_node: torch.fx.Node, delete_old: bool = False
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) -> None:
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"""
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Replace all uses of old_node with new_node.
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"""
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old_node.replace_all_uses_with(new_node)
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if delete_old:
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old_node.users.clear()
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old_node.graph.erase_node(old_node)
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def node_inline_(call_mod_node: torch.fx.Node) -> None:
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"""
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Inline the submodule of the given node into the parent module.
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Note: we only support the case where submodule takes tensors inputs.
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"""
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assert call_mod_node.op == "call_module"
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gm = call_mod_node.graph.owning_module
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assert isinstance(call_mod_node.target, str)
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sub_gm = getattr(gm, call_mod_node.target)
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phs = (node for node in sub_gm.graph.nodes if node.op == "placeholder")
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body = (
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node for node in sub_gm.graph.nodes if node.op not in ("placeholder", "output")
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)
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output = [node for node in sub_gm.graph.nodes if node.op == "output"]
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for ph, arg in zip(phs, call_mod_node.args):
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assert isinstance(arg, torch.fx.Node)
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node_replace_(ph, arg, delete_old=True)
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with gm.graph.inserting_before(call_mod_node):
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for node in body:
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new_node = gm.graph.node_copy(node)
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node_replace_(node, new_node, delete_old=True)
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if len(output) > 0:
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assert len(output) == 1 and len(output[0].args) == 1
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new_output = output[0].args[0]
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if isinstance(new_output, torch.fx.Node):
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node_replace_(call_mod_node, new_output, delete_old=True)
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elif isinstance(new_output, (list, tuple)):
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# Inline the get_item calls for the output node.
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get_item_users = nodes_filter(
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list(call_mod_node.users.keys()),
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lambda node: node.op == "call_function"
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and node.target == operator.getitem,
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)
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# get_item_node.args[1] is the idx referring to new_output[idx]
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nodes_map(
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get_item_users,
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lambda get_item_node: node_replace_(
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get_item_node,
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new_output[get_item_node.args[1]],
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delete_old=True,
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),
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)
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call_mod_node.graph.erase_node(call_mod_node)
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else:
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raise NotImplementedError(
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f"Unsupported output type {type(new_output)}. Expect it to be a Node or a list/tuple of Nodes."
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)
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else:
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call_mod_node.graph.erase_node(call_mod_node)
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gm.delete_all_unused_submodules()
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gm.recompile()
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return gm
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