torch/testing/_internal/schema_check_mode.py

import torch
from torch.utils._pytree import tree_flatten, tree_map
from torch.fx.operator_schemas import normalize_function
from torch.testing._internal.jit_utils import clone_inputs
from torch.utils._python_dispatch import TorchDispatchMode
from itertools import combinations
from collections import namedtuple
from copy import deepcopy

# Named Tuples used within SchemaCheckMode
Mutation = namedtuple('Mutation', ['op_name', 'arg_name'])
Aliasing = namedtuple('Aliasing', ['op_name', 'arg_name', 'output_number'])

# Simplified naming for C++ classes
SchemaArgument = torch._C._SchemaArgument
SchemaArgType = torch._C._SchemaArgType
SchemaInfo = torch._C._SchemaInfo

# This TorchDispatchMode Subclass is used to verify op schemas
# This TorchDispatchMode Scubclass currently:
#  - Records the called ops
#  - Checks for mutations on all inputs
#  - Checks for aliasing on all inputs

class SchemaCheckMode(TorchDispatchMode):
    def __init__(self):
        # Information recorded for testing purposes. For example:
        #  - incorrect schemas
        #  - overly conservative schemas
        self.ops = []
        self.mutated = []
        self.aliasing = []

    def reset_cache(self):
        self.ops.clear()
        self.mutated.clear()
        self.aliasing.clear()

    def display_ops(self):
        print(*self.ops, sep=",")

    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
        def has_mutated(before, after, md):
            are_tensors = type(before) == torch.Tensor and type(after) == torch.Tensor
            if are_tensors and before.layout != torch.sparse_csr and after.layout != torch.sparse_csr:
                return not (
                    before.size() == after.size() and
                    torch.allclose(before, after, equal_nan=True) and
                    md[0] == after.stride() and
                    md[1] == after.storage()._cdata
                )
            return False

        def has_aliased(lhs, rhs):
            try:
                return torch._C._overlaps(lhs, rhs)
            except Exception as exception:
                if str(exception).startswith("Cannot inspect value of type "):
                    return False
                else:
                    raise exception

        def standardize_name(name):
            return name if name != "self" else "input"

        def unwrap(e):
            if isinstance(e, torch.Tensor) and not type(e) == torch.Tensor:
                try:
                    return e.elem
                except AttributeError as t:
                    return e
            return e

        def parse_metadata(e):
            if isinstance(e, torch.Tensor):
                if not type(e) == torch.Tensor:
                    try:
                        current = e.elem
                        return (deepcopy(current.stride()), current.storage()._cdata)
                    except AttributeError as t:
                        return None
                # Sparse CSR tensors do not have strides or storage
                elif (e.layout != torch.sparse_csr):
                    return (deepcopy(e.stride()), e.storage()._cdata)
            return None

        self.ops.append(func._schema.name)

        # Clone and process arguments and outputs
        pre_arguments = normalize_function(
            func,
            args,
            kwargs,
            normalize_to_only_use_kwargs=True
        ).kwargs

        c_p_args = dict(zip(pre_arguments.keys(), clone_inputs(pre_arguments.values())))
        cloned_arguments = {name : tree_map(unwrap, c_p_args.get(name)) for name in c_p_args}
        cloned_metadata = {name : tree_map(parse_metadata, tree_flatten(pre_arguments.get(name))[0]) for name in pre_arguments}

        out = func(*args, **kwargs)
        arguments = {name : tree_map(unwrap, pre_arguments.get(name)) for name in pre_arguments}
        tuple_out = out if isinstance(out, tuple) else (out, )
        tuple_out = tree_map(unwrap, tuple_out)

        schema_info = SchemaInfo(func._schema)
        schema_info.add_argument_values(pre_arguments)

        # Process arguments with outputs
        for i in range(len(func._schema.arguments)):
            arg = func._schema.arguments[i]
            name = standardize_name(arg.name)
            if arguments.get(name) is not None:
                before = cloned_arguments.get(name)
                md = cloned_metadata.get(name)
                after = arguments.get(name)
                for j in range(len(tuple_out)):
                    # aten::_unsafe_view is intended to have incorrect aliasing notation (hence unsafe)
                    if has_aliased(tuple_out[j], after) and func._schema.name != 'aten::_unsafe_view':
                        if not schema_info.may_contain_alias(
                            SchemaArgument(SchemaArgType.output, j),
                                SchemaArgument(SchemaArgType.input, i)):
                            raise RuntimeError(f'Argument {name} is not defined to alias output but was aliasing')
                        else:
                            self.aliasing.append(Aliasing(func._schema.name, name, f"output_{j}"))
                if any(has_mutated(a, b, c) for a, b, c in zip(tree_flatten(before)[0], tree_flatten(after)[0], md)):
                    if not schema_info.is_mutable(SchemaArgument(SchemaArgType.input, i)):
                        raise RuntimeError(f"Argument {name} is not defined as mutable but was mutated")
                    else:
                        self.mutated.append(Mutation(func._schema.name, name))

        # Aliasing between outputs
        for i, j in combinations(range(len(func._schema.returns)), 2):
            if has_aliased(tuple_out[i], tuple_out[j]):
                if not schema_info.may_contain_alias(
                    SchemaArgument(SchemaArgType.output, i),
                        SchemaArgument(SchemaArgType.output, j)):
                    raise RuntimeError(f'Outputs {i} and {j} alias unexpectedly')

        return out