Merge pull request #1186 from ethereum/vbuterin-patch-3
Updates to SSZ Merkle proofs phase 0 doc
This commit is contained in:
Diederik Loerakker
GitHub
commit
f9d703c126
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@ -17,12 +17,6 @@
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<!-- /TOC -->
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## Constants
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| Name | Value |
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| - | - |
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| `LENGTH_FLAG` | `2**64 - 1` |
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## Generalized Merkle tree index
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In a binary Merkle tree, we define a "generalized index" of a node as `2**depth + index`. Visually, this looks as follows:
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@ -38,7 +32,8 @@ Note that the generalized index has the convenient property that the two childre
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```python
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def merkle_tree(leaves: List[Bytes32]) -> List[Bytes32]:
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o = [0] * len(leaves) + leaves
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padded_length = next_power_of_2(len(leaves))
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o = [ZERO_HASH] * padded_length + leaves + [ZERO_HASH] * (padded_length - len(leaves))
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for i in range(len(leaves) - 1, 0, -1):
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o[i] = hash(o[i * 2] + o[i * 2 + 1])
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return o
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@ -64,43 +59,134 @@ y_data_root len(y)
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We can now define a concept of a "path", a way of describing a function that takes as input an SSZ object and outputs some specific (possibly deeply nested) member. For example, `foo -> foo.x` is a path, as are `foo -> len(foo.y)` and `foo -> foo.y[5].w`. We'll describe paths as lists, which can have two representations. In "human-readable form", they are `["x"]`, `["y", "__len__"]` and `["y", 5, "w"]` respectively. In "encoded form", they are lists of `uint64` values, in these cases (assuming the fields of `foo` in order are `x` then `y`, and `w` is the first field of `y[i]`) `[0]`, `[1, 2**64-1]`, `[1, 5, 0]`.
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```python
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def path_to_encoded_form(obj: Any, path: List[Union[str, int]]) -> List[int]:
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if len(path) == 0:
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return []
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elif isinstance(path[0], "__len__"):
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assert len(path) == 1
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return [LENGTH_FLAG]
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elif isinstance(path[0], str) and hasattr(obj, "fields"):
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return [list(obj.fields.keys()).index(path[0])] + path_to_encoded_form(getattr(obj, path[0]), path[1:])
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elif isinstance(obj, (Vector, List)):
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return [path[0]] + path_to_encoded_form(obj[path[0]], path[1:])
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def item_length(typ: SSZType) -> int:
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"""
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Returns the number of bytes in a basic type, or 32 (a full hash) for compound types.
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"""
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if issubclass(typ, BasicValue):
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return typ.byte_len
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else:
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raise Exception("Unknown type / path")
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return 32
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def get_elem_type(typ: ComplexType, index: Union[int, str]) -> Type:
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"""
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Returns the type of the element of an object of the given type with the given index
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or member variable name (eg. `7` for `x[7]`, `"foo"` for `x.foo`)
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"""
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return typ.get_fields()[index] if issubclass(typ, Container) else typ.elem_type
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def chunk_count(typ: SSZType) -> int:
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"""
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Returns the number of hashes needed to represent the top-level elements in the given type
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(eg. `x.foo` or `x[7]` but not `x[7].bar` or `x.foo.baz`). In all cases except lists/vectors
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of basic types, this is simply the number of top-level elements, as each element gets one
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hash. For lists/vectors of basic types, it is often fewer because multiple basic elements
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can be packed into one 32-byte chunk.
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"""
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# typ.length describes the limit for list types, or the length for vector types.
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if issubclass(typ, BasicValue):
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return 1
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elif issubclass(typ, Bits):
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return (typ.length + 255) // 256
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elif issubclass(typ, Elements):
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return (typ.length * item_length(typ.elem_type) + 31) // 32
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elif issubclass(typ, Container):
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return len(typ.get_fields())
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else:
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raise Exception(f"Type not supported: {typ}")
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def get_item_position(typ: SSZType, index: Union[int, str]) -> Tuple[int, int, int]:
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"""
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Returns three variables: (i) the index of the chunk in which the given element of the item is
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represented, (ii) the starting byte position within the chunk, (iii) the ending byte position within the chunk. For example for
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a 6-item list of uint64 values, index=2 will return (0, 16, 24), index=5 will return (1, 8, 16)
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"""
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if issubclass(typ, Elements):
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start = index * item_length(typ.elem_type)
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return start // 32, start % 32, start % 32 + item_length(typ.elem_type)
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elif issubclass(typ, Container):
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return typ.get_field_names().index(index), 0, item_length(get_elem_type(typ, index))
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else:
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raise Exception("Only lists/vectors/containers supported")
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def get_generalized_index(typ: Type, path: List[Union[int, str]]) -> GeneralizedIndex:
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"""
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Converts a path (eg. `[7, "foo", 3]` for `x[7].foo[3]`, `[12, "bar", "__len__"]` for
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`len(x[12].bar)`) into the generalized index representing its position in the Merkle tree.
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"""
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root = 1
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for p in path:
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assert not issubclass(typ, BasicValue) # If we descend to a basic type, the path cannot continue further
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if p == '__len__':
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typ, root = uint64, root * 2 + 1 if issubclass(typ, (List, Bytes)) else None
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else:
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pos, _, _ = get_item_position(typ, p)
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root = root * (2 if issubclass(typ, (List, Bytes)) else 1) * next_power_of_two(chunk_count(typ)) + pos
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typ = get_elem_type(typ, p)
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return root
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```
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We can now define a function `get_generalized_indices(object: Any, path: List[int], root: int=1) -> List[int]` that converts an object and a path to a set of generalized indices (note that for constant-sized objects, there is only one generalized index and it only depends on the path, but for dynamically sized objects the indices may depend on the object itself too). For dynamically-sized objects, the set of indices will have more than one member because of the need to access an array's length to determine the correct generalized index for some array access.
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### Helpers for generalized indices
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_Usage note: functions outside this section should manipulate generalized indices using only functions inside this section. This is to make it easier for developers to implement generalized indices with underlying representations other than bigints._
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#### `concat_generalized_indices`
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```python
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def get_generalized_indices(obj: Any, path: List[int], root: int=1) -> List[int]:
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if len(path) == 0:
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return [root]
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elif isinstance(obj, Vector):
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items_per_chunk = (32 // len(serialize(x))) if isinstance(x, int) else 1
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new_root = root * next_power_of_2(len(obj) // items_per_chunk) + path[0] // items_per_chunk
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return get_generalized_indices(obj[path[0]], path[1:], new_root)
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elif isinstance(obj, List) and path[0] == LENGTH_FLAG:
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return [root * 2 + 1]
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elif isinstance(obj, List) and isinstance(path[0], int):
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assert path[0] < len(obj)
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items_per_chunk = (32 // len(serialize(x))) if isinstance(x, int) else 1
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new_root = root * 2 * next_power_of_2(len(obj) // items_per_chunk) + path[0] // items_per_chunk
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return [root *2 + 1] + get_generalized_indices(obj[path[0]], path[1:], new_root)
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elif hasattr(obj, "fields"):
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field = list(fields.keys())[path[0]]
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new_root = root * next_power_of_2(len(fields)) + path[0]
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return get_generalized_indices(getattr(obj, field), path[1:], new_root)
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else:
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raise Exception("Unknown type / path")
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def concat_generalized_indices(*indices: Sequence[GeneralizedIndex]) -> GeneralizedIndex:
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"""
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Given generalized indices i1 for A -> B, i2 for B -> C .... i_n for Y -> Z, returns
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the generalized index for A -> Z.
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"""
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o = GeneralizedIndex(1)
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for i in indices:
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o = o * get_previous_power_of_2(i) + (i - get_previous_power_of_2(i))
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return o
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```
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#### `get_generalized_index_length`
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```python
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def get_generalized_index_length(index: GeneralizedIndex) -> int:
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"""
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Returns the length of a path represented by a generalized index.
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"""
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return log2(index)
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```
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#### `get_generalized_index_bit`
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```python
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def get_generalized_index_bit(index: GeneralizedIndex, position: int) -> bool:
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"""
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Returns the given bit of a generalized index.
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"""
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return (index & (1 << position)) > 0
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```
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#### `generalized_index_sibling`
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```python
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def generalized_index_sibling(index: GeneralizedIndex) -> GeneralizedIndex:
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return index ^ 1
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```
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#### `generalized_index_child`
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```python
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def generalized_index_child(index: GeneralizedIndex, right_side: bool) -> GeneralizedIndex:
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return index * 2 + right_side
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```
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#### `generalized_index_parent`
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```python
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def generalized_index_parent(index: GeneralizedIndex) -> GeneralizedIndex:
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return index // 2
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```
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## Merkle multiproofs
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@ -116,72 +202,69 @@ x x . . . . x *
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. are unused nodes, * are used nodes, x are the values we are trying to prove. Notice how despite being a multiproof for 3 values, it requires only 3 auxiliary nodes, only one node more than would be required to prove a single value. Normally the efficiency gains are not quite that extreme, but the savings relative to individual Merkle proofs are still significant. As a rule of thumb, a multiproof for k nodes at the same level of an n-node tree has size `k * (n/k + log(n/k))`.
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Here is code for creating and verifying a multiproof. First, a method for computing the generalized indices of the auxiliary tree nodes that a proof of a given set of generalized indices will require:
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First, we provide a method for computing the generalized indices of the auxiliary tree nodes that a proof of a given set of generalized indices will require:
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```python
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def get_proof_indices(tree_indices: List[int]) -> List[int]:
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# Get all indices touched by the proof
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maximal_indices = set()
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for i in tree_indices:
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x = i
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while x > 1:
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maximal_indices.add(x ^ 1)
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x //= 2
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maximal_indices = tree_indices + sorted(list(maximal_indices))[::-1]
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# Get indices that cannot be recalculated from earlier indices
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redundant_indices = set()
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proof = []
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for index in maximal_indices:
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if index not in redundant_indices:
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proof.append(index)
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while index > 1:
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redundant_indices.add(index)
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if (index ^ 1) not in redundant_indices:
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break
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index //= 2
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return [i for i in proof if i not in tree_indices]
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def get_branch_indices(tree_index: GeneralizedIndex) -> List[GeneralizedIndex]:
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"""
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Get the generalized indices of the sister chunks along the path from the chunk with the
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given tree index to the root.
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"""
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o = [generalized_index_sibling(tree_index)]
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while o[-1] > 1:
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o.append(generalized_index_sibling(generalized_index_parent(o[-1])))
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return o[:-1]
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def get_helper_indices(indices: List[GeneralizedIndex]) -> List[GeneralizedIndex]:
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"""
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Get the generalized indices of all "extra" chunks in the tree needed to prove the chunks with the given
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generalized indices. Note that the decreasing order is chosen deliberately to ensure equivalence to the
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order of hashes in a regular single-item Merkle proof in the single-item case.
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"""
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all_indices = set()
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for index in indices:
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all_indices = all_indices.union(set(get_branch_indices(index) + [index]))
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return sorted([
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x for x in all_indices if not
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(generalized_index_child(x, 0) in all_indices and generalized_index_child(x, 1) in all_indices) and not
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(x in indices)
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], reverse=True)
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```
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Generating a proof is simply a matter of taking the node of the SSZ hash tree with the union of the given generalized indices for each index given by `get_proof_indices`, and outputting the list of nodes in the same order.
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Here is the verification function:
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Now we provide the Merkle proof verification functions. First, for single item proofs:
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```python
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def verify_multi_proof(root: Bytes32, indices: List[int], leaves: List[Bytes32], proof: List[Bytes32]) -> bool:
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tree = {}
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for index, leaf in zip(indices, leaves):
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tree[index] = leaf
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for index, proof_item in zip(get_proof_indices(indices), proof):
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tree[index] = proof_item
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index_queue = sorted(tree.keys())[:-1]
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i = 0
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while i < len(index_queue):
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index = index_queue[i]
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if index >= 2 and index ^ 1 in tree:
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tree[index // 2] = hash(tree[index - index % 2] + tree[index - index % 2 + 1])
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index_queue.append(index // 2)
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i += 1
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return (indices == []) or (1 in tree and tree[1] == root)
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def verify_merkle_proof(leaf: Hash, proof: Sequence[Hash], index: GeneralizedIndex, root: Hash) -> bool:
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assert len(proof) == get_generalized_index_length(index)
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for i, h in enumerate(proof):
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if get_generalized_index_bit(index, i):
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leaf = hash(h + leaf)
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else:
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leaf = hash(leaf + h)
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return leaf == root
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```
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## MerklePartial
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We define:
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### `SSZMerklePartial`
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Now for multi-item proofs:
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```python
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{
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"root": "bytes32",
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"indices": ["uint64"],
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"values": ["bytes32"],
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"proof": ["bytes32"]
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def verify_merkle_multiproof(leaves: Sequence[Hash], proof: Sequence[Hash], indices: Sequence[GeneralizedIndex], root: Hash) -> bool:
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assert len(leaves) == len(indices)
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helper_indices = get_helper_indices(indices)
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assert len(proof) == len(helper_indices)
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objects = {
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**{index:node for index, node in zip(indices, leaves)},
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**{index:node for index, node in zip(helper_indices, proof)}
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}
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keys = sorted(objects.keys(), reverse=True)
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pos = 0
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while pos < len(keys):
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k = keys[pos]
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if k in objects and k ^ 1 in objects and k // 2 not in objects:
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objects[k // 2] = hash(objects[(k | 1) ^ 1] + objects[k | 1])
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keys.append(k // 2)
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pos += 1
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return objects[1] == root
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```
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### Proofs for execution
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We define `MerklePartial(f, arg1, arg2..., focus=0)` as being a `SSZMerklePartial` object wrapping a Merkle multiproof of the set of nodes in the hash tree of the SSZ object `arg[focus]` that is needed to authenticate the parts of the object needed to compute `f(arg1, arg2...)`.
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Ideally, any function which accepts an SSZ object should also be able to accept a `SSZMerklePartial` object as a substitute.
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Note that the single-item proof is a special case of a multi-item proof; a valid single-item proof verifies correctly when put into the multi-item verification function (making the natural trivial changes to input arguments, `index -> [index]` and `leaf -> [leaf]`).
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