Canonical ABI Explainer

This document defines the Canonical ABI used to convert between the values and functions of components in the Component Model and the values and functions of modules in Core WebAssembly.

Supporting definitions
Canonical definitions
- canon lift
- canon lower
Canonical ABI
- Canonical Module Type
- Lifting Canonical Modules

Supporting definitions

The Canonical ABI specifies, for each component function signature, a corresponding core function signature and the process for reading component-level values into and out of linear memory. While a full formal specification would specify the Canonical ABI in terms of macro-expansion into Core WebAssembly instructions augmented with a new set of (spec-internal) administrative instructions, the informal presentation here instead specifies the process in terms of Python code that would be logically executed at validation- and run-time by a component model implementation. The Python code is presented by interleaving definitions with descriptions and eliding some boilerplate. For a complete listing of all Python definitions in a single executable file with a small unit test suite, see the canonical-abi directory.

The convention followed by the Python code below is that all traps are raised by explicit trap()/trap_if() calls; Python assert() statements should never fire and are only included as hints to the reader. Similarly, there should be no uncaught Python exceptions.

While the Python code appears to perform a copy as part of lifting the contents of linear memory into high-level Python values, a normal implementation should never need to make this extra intermediate copy. This claim is expanded upon below.

Lastly, independently of Python, the Canonical ABI defined below assumes that out-of-memory conditions (such as memory.grow returning -1 from within realloc) will trap (via unreachable). This significantly simplifies the Canonical ABI by avoiding the need to support the complicated protocols necessary to support recovery in the middle of nested allocations. In the MVP, for large allocations that can OOM, streams would usually be the appropriate type to use and streams will be able to explicitly express failure in their type. Post-MVP, adapter functions would allow fully custom OOM handling for all component-level types, allowing a toolchain to intentionally propagate OOM into the appropriate explicit return value of the function's declared return type.

Despecialization

In the explainer, component value types are classified as either fundamental or specialized, where the specialized value types are defined by expansion into fundamental value types. In most cases, the canonical ABI of a specialized value type is the same as its expansion so, to avoid repetition, the other definitions below use the following despecialize function to replace specialized value types with their expansion:

def despecialize(t):
  match t:
    case Tuple(ts)         : return Record([ Field(str(i), t) for i,t in enumerate(ts) ])
    case Union(ts)         : return Variant([ Case(str(i), t) for i,t in enumerate(ts) ])
    case Enum(labels)      : return Variant([ Case(l, None) for l in labels ])
    case Option(t)         : return Variant([ Case("none", None), Case("some", t) ])
    case Result(ok, error) : return Variant([ Case("ok", ok), Case("error", error) ])
    case _                 : return t

The specialized value types string and flags are missing from this list because they are given specialized canonical ABI representations distinct from their respective expansions.

Alignment

Each value type is assigned an alignment which is used by subsequent Canonical ABI definitions. Presenting the definition of alignment piecewise, we start with the top-level case analysis:

def alignment(t):
  match despecialize(t):
    case Bool()             : return 1
    case S8() | U8()        : return 1
    case S16() | U16()      : return 2
    case S32() | U32()      : return 4
    case S64() | U64()      : return 8
    case Float32()          : return 4
    case Float64()          : return 8
    case Char()             : return 4
    case String() | List(_) : return 4
    case Record(fields)     : return alignment_record(fields)
    case Variant(cases)     : return alignment_variant(cases)
    case Flags(labels)      : return alignment_flags(labels)

Record alignment is tuple alignment, with the definitions split for reuse below:

def alignment_record(fields):
  a = 1
  for f in fields:
    a = max(a, alignment(f.t))
  return a

As an optimization, variant discriminants are represented by the smallest integer covering the number of cases in the variant. Depending on the payload type, this can allow more compact representations of variants in memory. This smallest integer type is selected by the following function, used above and below:

def alignment_variant(cases):
  return max(alignment(discriminant_type(cases)), max_case_alignment(cases))

def discriminant_type(cases):
  n = len(cases)
  assert(0 < n < (1 << 32))
  match math.ceil(math.log2(n)/8):
    case 0: return U8()
    case 1: return U8()
    case 2: return U16()
    case 3: return U32()

def max_case_alignment(cases):
  a = 1
  for c in cases:
    if c.t is not None:
      a = max(a, alignment(c.t))
  return a

As an optimization, flags are represented as packed bit-vectors. Like variant discriminants, flags use the smallest integer that fits all the bits, falling back to sequences of i32s when there are more than 32 flags.

def alignment_flags(labels):
  n = len(labels)
  if n <= 8: return 1
  if n <= 16: return 2
  return 4

Size

Each value type is also assigned a size, measured in bytes, which corresponds the sizeof operator in C:

def size(t):
  match despecialize(t):
    case Bool()             : return 1
    case S8() | U8()        : return 1
    case S16() | U16()      : return 2
    case S32() | U32()      : return 4
    case S64() | U64()      : return 8
    case Float32()          : return 4
    case Float64()          : return 8
    case Char()             : return 4
    case String() | List(_) : return 8
    case Record(fields)     : return size_record(fields)
    case Variant(cases)     : return size_variant(cases)
    case Flags(labels)      : return size_flags(labels)

def size_record(fields):
  s = 0
  for f in fields:
    s = align_to(s, alignment(f.t))
    s += size(f.t)
  return align_to(s, alignment_record(fields))

def align_to(ptr, alignment):
  return math.ceil(ptr / alignment) * alignment

def size_variant(cases):
  s = size(discriminant_type(cases))
  s = align_to(s, max_case_alignment(cases))
  cs = 0
  for c in cases:
    if c.t is not None:
      cs = max(cs, size(c.t))
  s += cs
  return align_to(s, alignment_variant(cases))

def size_flags(labels):
  n = len(labels)
  if n == 0: return 0
  if n <= 8: return 1
  if n <= 16: return 2
  return 4 * num_i32_flags(labels)

def num_i32_flags(labels):
  return math.ceil(len(labels) / 32)

Loading

The load function defines how to read a value of a given value type t out of linear memory starting at offset ptr, returning the value represented as a Python value. The Opts/opts class/parameter contains the canonopt immediates supplied as part of canon lift/canon lower. Presenting the definition of load piecewise, we start with the top-level case analysis:

class Opts:
  string_encoding: str
  memory: bytearray
  realloc: Callable[[int,int,int,int],int]
  post_return: Callable[[],None]

def load(opts, ptr, t):
  assert(ptr == align_to(ptr, alignment(t)))
  assert(ptr + size(t) <= len(opts.memory))
  match despecialize(t):
    case Bool()         : return convert_int_to_bool(load_int(opts, ptr, 1))
    case U8()           : return load_int(opts, ptr, 1)
    case U16()          : return load_int(opts, ptr, 2)
    case U32()          : return load_int(opts, ptr, 4)
    case U64()          : return load_int(opts, ptr, 8)
    case S8()           : return load_int(opts, ptr, 1, signed=True)
    case S16()          : return load_int(opts, ptr, 2, signed=True)
    case S32()          : return load_int(opts, ptr, 4, signed=True)
    case S64()          : return load_int(opts, ptr, 8, signed=True)
    case Float32()      : return canonicalize32(reinterpret_i32_as_float(load_int(opts, ptr, 4)))
    case Float64()      : return canonicalize64(reinterpret_i64_as_float(load_int(opts, ptr, 8)))
    case Char()         : return i32_to_char(opts, load_int(opts, ptr, 4))
    case String()       : return load_string(opts, ptr)
    case List(t)        : return load_list(opts, ptr, t)
    case Record(fields) : return load_record(opts, ptr, fields)
    case Variant(cases) : return load_variant(opts, ptr, cases)
    case Flags(labels)  : return load_flags(opts, ptr, labels)

Integers are loaded directly from memory, with their high-order bit interpreted according to the signedness of the type.

def load_int(opts, ptr, nbytes, signed = False):
  trap_if(ptr + nbytes > len(opts.memory))
  return int.from_bytes(opts.memory[ptr : ptr+nbytes], 'little', signed=signed)

Integer-to-boolean conversions treats 0 as false and all other bit-patterns as true:

def convert_int_to_bool(i):
  assert(i >= 0)
  return bool(i)

For reasons given in the explainer, floats are loaded from memory and then "canonicalized", mapping all Not-a-Number bit patterns to a single canonical nan value.

def reinterpret_i32_as_float(i):
  return struct.unpack('!f', struct.pack('!I', i))[0] # f32.reinterpret_i32

def reinterpret_i64_as_float(i):
  return struct.unpack('!d', struct.pack('!Q', i))[0] # f64.reinterpret_i64

CANONICAL_FLOAT32_NAN = 0x7fc00000
CANONICAL_FLOAT64_NAN = 0x7ff8000000000000

def canonicalize32(f):
  if math.isnan(f):
    return reinterpret_i32_as_float(CANONICAL_FLOAT32_NAN)
  return f

def canonicalize64(f):
  if math.isnan(f):
    return reinterpret_i64_as_float(CANONICAL_FLOAT64_NAN)
  return f

An i32 is converted to a char (a Unicode Scalar Value) by dynamically testing that its unsigned integral value is in the valid Unicode Code Point range and not a Surrogate:

def i32_to_char(opts, i):
  trap_if(i >= 0x110000)
  trap_if(0xD800 <= i <= 0xDFFF)
  return chr(i)

Strings are loaded from two i32 values: a pointer (offset in linear memory) and a number of bytes. There are three supported string encodings in canonopt: UTF-8, UTF-16 and latin1+utf16. This last options allows a dynamic choice between Latin-1 and UTF-16, indicated by the high bit of the second i32. String values include their original encoding and byte length as a "hint" that enables store_string (defined below) to make better up-front allocation size choices in many cases. Thus, the value produced by load_string isn't simply a Python str, but a tuple containing a str, the original encoding and the original byte length.

def load_string(opts, ptr):
  begin = load_int(opts, ptr, 4)
  tagged_code_units = load_int(opts, ptr + 4, 4)
  return load_string_from_range(opts, begin, tagged_code_units)

UTF16_TAG = 1 << 31

def load_string_from_range(opts, ptr, tagged_code_units):
  match opts.string_encoding:
    case 'utf8':
      alignment = 1
      byte_length = tagged_code_units
      encoding = 'utf-8'
    case 'utf16':
      alignment = 2
      byte_length = 2 * tagged_code_units
      encoding = 'utf-16-le'
    case 'latin1+utf16':
      alignment = 2
      if bool(tagged_code_units & UTF16_TAG):
        byte_length = 2 * (tagged_code_units ^ UTF16_TAG)
        encoding = 'utf-16-le'
      else:
        byte_length = tagged_code_units
        encoding = 'latin-1'

  trap_if(ptr != align_to(ptr, alignment))
  trap_if(ptr + byte_length > len(opts.memory))
  try:
    s = opts.memory[ptr : ptr+byte_length].decode(encoding)
  except UnicodeError:
    trap()

  return (s, opts.string_encoding, tagged_code_units)

Lists and records are loaded by recursively loading their elements/fields:

def load_list(opts, ptr, elem_type):
  begin = load_int(opts, ptr, 4)
  length = load_int(opts, ptr + 4, 4)
  return load_list_from_range(opts, begin, length, elem_type)

def load_list_from_range(opts, ptr, length, elem_type):
  trap_if(ptr != align_to(ptr, alignment(elem_type)))
  trap_if(ptr + length * size(elem_type) > len(opts.memory))
  a = []
  for i in range(length):
    a.append(load(opts, ptr + i * size(elem_type), elem_type))
  return a

def load_record(opts, ptr, fields):
  record = {}
  for field in fields:
    ptr = align_to(ptr, alignment(field.t))
    record[field.label] = load(opts, ptr, field.t)
    ptr += size(field.t)
  return record

As a technical detail: the align_to in the loop in load_record is guaranteed to be a no-op on the first iteration because the record as a whole starts out aligned (as asserted at the top of load).

Variants are loaded using the order of the cases in the type to determine the case index. To support the subtyping allowed by refines, a lifted variant value semantically includes a full ordered list of its refines case labels so that the lowering code (defined below) can search this list to find a case label it knows about. While the code below appears to perform case-label lookup at runtime, a normal implementation can build the appropriate index tables at compile-time so that variant-passing is always O(1) and not involving string operations.

def load_variant(opts, ptr, cases):
  disc_size = size(discriminant_type(cases))
  case_index = load_int(opts, ptr, disc_size)
  ptr += disc_size
  trap_if(case_index >= len(cases))
  c = cases[case_index]
  ptr = align_to(ptr, max_case_alignment(cases))
  case_label = case_label_with_refinements(c, cases)
  if c.t is None:
    return { case_label: None }
  return { case_label: load(opts, ptr, c.t) }

def case_label_with_refinements(c, cases):
  label = c.label
  while c.refines is not None:
    c = cases[find_case(c.refines, cases)]
    label += '|' + c.label
  return label

def find_case(label, cases):
  matches = [i for i,c in enumerate(cases) if c.label == label]
  assert(len(matches) <= 1)
  if len(matches) == 1:
    return matches[0]
  return -1

Finally, flags are converted from a bit-vector to a dictionary whose keys are derived from the ordered labels of the flags type. The code here takes advantage of Python's support for integers of arbitrary width.

def load_flags(opts, ptr, labels):
  i = load_int(opts, ptr, size_flags(labels))
  return unpack_flags_from_int(i, labels)

def unpack_flags_from_int(i, labels):
  record = {}
  for l in labels:
    record[l] = bool(i & 1)
    i >>= 1
  return record

Storing

The store function defines how to write a value v of a given value type t into linear memory starting at offset ptr. Presenting the definition of store piecewise, we start with the top-level case analysis:

def store(opts, v, t, ptr):
  assert(ptr == align_to(ptr, alignment(t)))
  assert(ptr + size(t) <= len(opts.memory))
  match despecialize(t):
    case Bool()         : store_int(opts, int(bool(v)), ptr, 1)
    case U8()           : store_int(opts, v, ptr, 1)
    case U16()          : store_int(opts, v, ptr, 2)
    case U32()          : store_int(opts, v, ptr, 4)
    case U64()          : store_int(opts, v, ptr, 8)
    case S8()           : store_int(opts, v, ptr, 1, signed=True)
    case S16()          : store_int(opts, v, ptr, 2, signed=True)
    case S32()          : store_int(opts, v, ptr, 4, signed=True)
    case S64()          : store_int(opts, v, ptr, 8, signed=True)
    case Float32()      : store_int(opts, reinterpret_float_as_i32(canonicalize32(v)), ptr, 4)
    case Float64()      : store_int(opts, reinterpret_float_as_i64(canonicalize64(v)), ptr, 8)
    case Char()         : store_int(opts, char_to_i32(v), ptr, 4)
    case String()       : store_string(opts, v, ptr)
    case List(t)        : store_list(opts, v, ptr, t)
    case Record(fields) : store_record(opts, v, ptr, fields)
    case Variant(cases) : store_variant(opts, v, ptr, cases)
    case Flags(labels)  : store_flags(opts, v, ptr, labels)

Integers are stored directly into memory. Because the input domain is exactly the integers in range for the given type, no extra range checks are necessary; the signed parameter is only present to ensure that the internal range checks of int.to_bytes are satisfied.

def store_int(opts, v, ptr, nbytes, signed = False):
  trap_if(ptr + nbytes > len(opts.memory))
  opts.memory[ptr : ptr+nbytes] = int.to_bytes(v, nbytes, 'little', signed=signed)

Floats are stored directly into memory (in the case of NaNs, using the 32-/64-bit canonical NaN bit pattern selected by canonicalize32/canonicalize64):

def reinterpret_float_as_i32(f):
  return struct.unpack('!I', struct.pack('!f', f))[0] # i32.reinterpret_f32

def reinterpret_float_as_i64(f):
  return struct.unpack('!Q', struct.pack('!d', f))[0] # i64.reinterpret_f64

The integral value of a char (a Unicode Scalar Value) is a valid unsigned i32 and thus no runtime conversion or checking is necessary:

def char_to_i32(c):
  i = ord(c)
  assert(0 <= i <= 0xD7FF or 0xD800 <= i <= 0x10FFFF)
  return i

Storing strings is complicated by the goal of attempting to optimize the different transcoding cases. In particular, one challenge is choosing the linear memory allocation size before examining the contents of the string. The reason for this constraint is that, in some settings where single-pass iterators are involved (host calls and post-MVP adapter functions), examining the contents of a string more than once would require making an engine-internal temporary copy of the whole string, which the component model specifically aims not to do. To avoid multiple passes, the canonical ABI instead uses a realloc approach to update the allocation size during the single copy. A blind realloc approach would normally suffer from multiple reallocations per string (e.g., using the standard doubling-growth strategy). However, as already shown in load_string above, string values come with two useful hints: their original encoding and byte length. From this hint data, store_string can do a much better job minimizing the number of reallocations.

We start with a case analysis to enumerate all the meaningful encoding combinations, subdividing the latin1+utf16 encoding into either latin1 or utf16 based on the UTF16_BIT flag set by load_string:

def store_string(opts, v, ptr):
  begin, tagged_code_units = store_string_into_range(opts, v)
  store_int(opts, begin, ptr, 4)
  store_int(opts, tagged_code_units, ptr + 4, 4)

def store_string_into_range(opts, v):
  src, src_encoding, src_tagged_code_units = v

  if src_encoding == 'latin1+utf16':
    if bool(src_tagged_code_units & UTF16_TAG):
      src_simple_encoding = 'utf16'
      src_code_units = src_tagged_code_units ^ UTF16_TAG
    else:
      src_simple_encoding = 'latin1'
      src_code_units = src_tagged_code_units
  else:
    src_simple_encoding = src_encoding
    src_code_units = src_tagged_code_units

  match opts.string_encoding:
    case 'utf8':
      match src_simple_encoding:
        case 'utf8'         : return store_string_copy(opts, src, src_code_units, 1, 1, 'utf-8')
        case 'utf16'        : return store_utf16_to_utf8(opts, src, src_code_units)
        case 'latin1'       : return store_latin1_to_utf8(opts, src, src_code_units)
    case 'utf16':
      match src_simple_encoding:
        case 'utf8'         : return store_utf8_to_utf16(opts, src, src_code_units)
        case 'utf16'        : return store_string_copy(opts, src, src_code_units, 2, 2, 'utf-16-le')
        case 'latin1'       : return store_string_copy(opts, src, src_code_units, 2, 2, 'utf-16-le')
    case 'latin1+utf16':
      match src_encoding:
        case 'utf8'         : return store_string_to_latin1_or_utf16(opts, src, src_code_units)
        case 'utf16'        : return store_string_to_latin1_or_utf16(opts, src, src_code_units)
        case 'latin1+utf16' :
          match src_simple_encoding:
            case 'latin1'   : return store_string_copy(opts, src, src_code_units, 1, 2, 'latin-1')
            case 'utf16'    : return store_probably_utf16_to_latin1_or_utf16(opts, src, src_code_units)

The simplest 4 cases above can compute the exact destination size and then copy with a simply loop (that possibly inflates Latin-1 to UTF-16 by injecting a 0 byte after every Latin-1 byte).

MAX_STRING_BYTE_LENGTH = (1 << 31) - 1

def store_string_copy(opts, src, src_code_units, dst_code_unit_size, dst_alignment, dst_encoding):
  dst_byte_length = dst_code_unit_size * src_code_units
  trap_if(dst_byte_length > MAX_STRING_BYTE_LENGTH)
  ptr = opts.realloc(0, 0, dst_alignment, dst_byte_length)
  trap_if(ptr != align_to(ptr, dst_alignment))
  trap_if(ptr + dst_byte_length > len(opts.memory))
  encoded = src.encode(dst_encoding)
  assert(dst_byte_length == len(encoded))
  opts.memory[ptr : ptr+len(encoded)] = encoded
  return (ptr, src_code_units)

The choice of MAX_STRING_BYTE_LENGTH constant ensures that the high bit of a string's byte length is never set, keeping it clear for UTF16_BIT.

The 2 cases of transcoding into UTF-8 share an algorithm that starts by optimistically assuming that each code unit of the source string fits in a single UTF-8 byte and then, failing that, reallocates to a worst-case size, finishes the copy, and then finishes with a shrinking reallocation.

def store_utf16_to_utf8(opts, src, src_code_units):
  worst_case_size = src_code_units * 3
  return store_string_to_utf8(opts, src, src_code_units, worst_case_size)

def store_latin1_to_utf8(opts, src, src_code_units):
  worst_case_size = src_code_units * 2
  return store_string_to_utf8(opts, src, src_code_units, worst_case_size)

def store_string_to_utf8(opts, src, src_code_units, worst_case_size):
  assert(src_code_units <= MAX_STRING_BYTE_LENGTH)
  ptr = opts.realloc(0, 0, 1, src_code_units)
  trap_if(ptr + src_code_units > len(opts.memory))
  encoded = src.encode('utf-8')
  assert(src_code_units <= len(encoded))
  opts.memory[ptr : ptr+src_code_units] = encoded[0 : src_code_units]
  if src_code_units < len(encoded):
    trap_if(worst_case_size > MAX_STRING_BYTE_LENGTH)
    ptr = opts.realloc(ptr, src_code_units, 1, worst_case_size)
    trap_if(ptr + worst_case_size > len(opts.memory))
    opts.memory[ptr+src_code_units : ptr+len(encoded)] = encoded[src_code_units : ]
    if worst_case_size > len(encoded):
      ptr = opts.realloc(ptr, worst_case_size, 1, len(encoded))
      trap_if(ptr + len(encoded) > len(opts.memory))
  return (ptr, len(encoded))

Converting from UTF-8 to UTF-16 performs an initial worst-case size allocation (assuming each UTF-8 byte encodes a whole code point that inflates into a two-byte UTF-16 code unit) and then does a shrinking reallocation at the end if multiple UTF-8 bytes were collapsed into a single 2-byte UTF-16 code unit:

def store_utf8_to_utf16(opts, src, src_code_units):
  worst_case_size = 2 * src_code_units
  trap_if(worst_case_size > MAX_STRING_BYTE_LENGTH)
  ptr = opts.realloc(0, 0, 2, worst_case_size)
  trap_if(ptr != align_to(ptr, 2))
  trap_if(ptr + worst_case_size > len(opts.memory))
  encoded = src.encode('utf-16-le')
  opts.memory[ptr : ptr+len(encoded)] = encoded
  if len(encoded) < worst_case_size:
    ptr = opts.realloc(ptr, worst_case_size, 2, len(encoded))
    trap_if(ptr != align_to(ptr, 2))
    trap_if(ptr + len(encoded) > len(opts.memory))
  code_units = int(len(encoded) / 2)
  return (ptr, code_units)

The next transcoding case handles latin1+utf16 encoding, where there general goal is to fit the incoming string into Latin-1 if possible based on the code points of the incoming string. The algorithm speculates that all code points do fit into Latin-1 and then falls back to a worst-case allocation size when a code point is found outside Latin-1. In this fallback case, the previously-copied Latin-1 bytes are inflated in place, inserting a 0 byte after every Latin-1 byte (iterating in reverse to avoid clobbering later bytes):

def store_string_to_latin1_or_utf16(opts, src, src_code_units):
  assert(src_code_units <= MAX_STRING_BYTE_LENGTH)
  ptr = opts.realloc(0, 0, 2, src_code_units)
  trap_if(ptr != align_to(ptr, 2))
  trap_if(ptr + src_code_units > len(opts.memory))
  dst_byte_length = 0
  for usv in src:
    if ord(usv) < (1 << 8):
      opts.memory[ptr + dst_byte_length] = ord(usv)
      dst_byte_length += 1
    else:
      worst_case_size = 2 * src_code_units
      trap_if(worst_case_size > MAX_STRING_BYTE_LENGTH)
      ptr = opts.realloc(ptr, src_code_units, 2, worst_case_size)
      trap_if(ptr != align_to(ptr, 2))
      trap_if(ptr + worst_case_size > len(opts.memory))
      for j in range(dst_byte_length-1, -1, -1):
        opts.memory[ptr + 2*j] = opts.memory[ptr + j]
        opts.memory[ptr + 2*j + 1] = 0
      encoded = src.encode('utf-16-le')
      opts.memory[ptr+2*dst_byte_length : ptr+len(encoded)] = encoded[2*dst_byte_length : ]
      if worst_case_size > len(encoded):
        ptr = opts.realloc(ptr, worst_case_size, 2, len(encoded))
        trap_if(ptr != align_to(ptr, 2))
        trap_if(ptr + len(encoded) > len(opts.memory))
      tagged_code_units = int(len(encoded) / 2) | UTF16_TAG
      return (ptr, tagged_code_units)
  if dst_byte_length < src_code_units:
    ptr = opts.realloc(ptr, src_code_units, 2, dst_byte_length)
    trap_if(ptr != align_to(ptr, 2))
    trap_if(ptr + dst_byte_length > len(opts.memory))
  return (ptr, dst_byte_length)

The final transcoding case takes advantage of the extra heuristic information that the incoming UTF-16 bytes were intentionally chosen over Latin-1 by the producer, indicating that they probably contain code points outside Latin-1 and thus probably require inflation. Based on this information, the transcoding algorithm pessimistically allocates storage for UTF-16, deflating at the end if indeed no non-Latin-1 code points were encountered. This Latin-1 deflation ensures that if a group of components are all using latin1+utf16 and one component over-uses UTF-16, other components can recover the Latin-1 compression. (The Latin-1 check can be inexpensively fused with the UTF-16 validate+copy loop.)

def store_probably_utf16_to_latin1_or_utf16(opts, src, src_code_units):
  src_byte_length = 2 * src_code_units
  trap_if(src_byte_length > MAX_STRING_BYTE_LENGTH)
  ptr = opts.realloc(0, 0, 2, src_byte_length)
  trap_if(ptr != align_to(ptr, 2))
  trap_if(ptr + src_byte_length > len(opts.memory))
  encoded = src.encode('utf-16-le')
  opts.memory[ptr : ptr+len(encoded)] = encoded
  if any(ord(c) >= (1 << 8) for c in src):
    tagged_code_units = int(len(encoded) / 2) | UTF16_TAG
    return (ptr, tagged_code_units)
  latin1_size = int(len(encoded) / 2)
  for i in range(latin1_size):
    opts.memory[ptr + i] = opts.memory[ptr + 2*i]
  ptr = opts.realloc(ptr, src_byte_length, 1, latin1_size)
  trap_if(ptr + latin1_size > len(opts.memory))
  return (ptr, latin1_size)

Lists and records are stored by recursively storing their elements and are symmetric to the loading functions. Unlike strings, lists can simply allocate based on the up-front knowledge of length and static element size.

def store_list(opts, v, ptr, elem_type):
  begin, length = store_list_into_range(opts, v, elem_type)
  store_int(opts, begin, ptr, 4)
  store_int(opts, length, ptr + 4, 4)

def store_list_into_range(opts, v, elem_type):
  byte_length = len(v) * size(elem_type)
  trap_if(byte_length >= (1 << 32))
  ptr = opts.realloc(0, 0, alignment(elem_type), byte_length)
  trap_if(ptr != align_to(ptr, alignment(elem_type)))
  trap_if(ptr + byte_length > len(opts.memory))
  for i,e in enumerate(v):
    store(opts, e, elem_type, ptr + i * size(elem_type))
  return (ptr, len(v))

def store_record(opts, v, ptr, fields):
  for f in fields:
    ptr = align_to(ptr, alignment(f.t))
    store(opts, v[f.label], f.t, ptr)
    ptr += size(f.t)

Variants are stored using the |-separated list of refines cases built by case_label_with_refinements (above) to iteratively find a matching case (which validation guarantees will succeed). While this code appears to do O(n) string matching, a normal implementation can statically fuse store_variant with its matching load_variant to ultimately build a dense array that maps producer's case indices to the consumer's case indices.

def store_variant(opts, v, ptr, cases):
  case_index, case_value = match_case(v, cases)
  disc_size = size(discriminant_type(cases))
  store_int(opts, case_index, ptr, disc_size)
  ptr += disc_size
  ptr = align_to(ptr, max_case_alignment(cases))
  c = cases[case_index]
  if c.t is not None:
    store(opts, case_value, c.t, ptr)

def match_case(v, cases):
  assert(len(v.keys()) == 1)
  key = list(v.keys())[0]
  value = list(v.values())[0]
  for label in key.split('|'):
    case_index = find_case(label, cases)
    if case_index != -1:
      return (case_index, value)

Finally, flags are converted from a dictionary to a bit-vector by iterating through the case-labels of the variant in the order they were listed in the type definition and OR-ing all the bits together. Flag lifting/lowering can be statically fused into array/integer operations (with a simple byte copy when the case lists are the same) to avoid any string operations in a similar manner to variants.

def store_flags(opts, v, ptr, labels):
  i = pack_flags_into_int(v, labels)
  store_int(opts, i, ptr, size_flags(labels))

def pack_flags_into_int(v, labels):
  i = 0
  shift = 0
  for l in labels:
    i |= (int(bool(v[l])) << shift)
    shift += 1
  return i

Flattening

With only the definitions above, the Canonical ABI would be forced to place all parameters and results in linear memory. While this is necessary in the general case, in many cases performance can be improved by passing small-enough values in registers by using core function parameters and results. To support this optimization, the Canonical ABI defines flatten to map component function types to core function types by attempting to decompose all the non-dynamically-sized component value types into core value types.

For a variety of practical reasons, we need to limit the total number of flattened parameters and results, falling back to storing everything in linear memory. The number of flattened results is currently limited to 1 due to various parts of the toolchain (notably the C ABI) not yet being able to express multi-value returns. Hopefully this limitation is temporary and can be lifted before the Component Model is fully standardized.

When there are too many flat values, in general, a single i32 pointer can be passed instead (pointing to a tuple in linear memory). When lowering into linear memory, this requires the Canonical ABI to call realloc (in lower below) to allocate space to put the tuple. As an optimization, when lowering the return value of an imported function (via canon lower), the caller can have already allocated space for the return value (e.g., efficiently on the stack), passing in an i32 pointer as an parameter instead of returning an i32 as a return value.

Given all this, the top-level definition of flatten is:

MAX_FLAT_PARAMS = 16
MAX_FLAT_RESULTS = 1

def flatten_functype(ft, context):
  flat_params = flatten_types(ft.param_types())
  if len(flat_params) > MAX_FLAT_PARAMS:
    flat_params = ['i32']

  flat_results = flatten_types(ft.result_types())
  if len(flat_results) > MAX_FLAT_RESULTS:
    match context:
      case 'lift':
        flat_results = ['i32']
      case 'lower':
        flat_params += ['i32']
        flat_results = []

  return CoreFuncType(flat_params, flat_results)

def flatten_types(ts):
  return [ft for t in ts for ft in flatten_type(t)]

Presenting the definition of flatten_type piecewise, we start with the top-level case analysis:

def flatten_type(t):
  match despecialize(t):
    case Bool()               : return ['i32']
    case U8() | U16() | U32() : return ['i32']
    case S8() | S16() | S32() : return ['i32']
    case S64() | U64()        : return ['i64']
    case Float32()            : return ['f32']
    case Float64()            : return ['f64']
    case Char()               : return ['i32']
    case String() | List(_)   : return ['i32', 'i32']
    case Record(fields)       : return flatten_record(fields)
    case Variant(cases)       : return flatten_variant(cases)
    case Flags(labels)        : return ['i32'] * num_i32_flags(labels)

Record flattening simply flattens each field in sequence.

def flatten_record(fields):
  flat = []
  for f in fields:
    flat += flatten_type(f.t)
  return flat

Variant flattening is more involved due to the fact that each case payload can have a totally different flattening. Rather than giving up when there is a type mismatch, the Canonical ABI relies on the fact that the 4 core value types can be easily bit-cast between each other and defines a join operator to pick the tightest approximation. What this means is that, regardless of the dynamic case, all flattened variants are passed with the same static set of core types, which may involve, e.g., reinterpreting an f32 as an i32 or zero-extending an i32 into an i64.

def flatten_variant(cases):
  flat = []
  for c in cases:
    if c.t is not None:
      for i,ft in enumerate(flatten_type(c.t)):
        if i < len(flat):
          flat[i] = join(flat[i], ft)
        else:
          flat.append(ft)
  return flatten_type(discriminant_type(cases)) + flat

def join(a, b):
  if a == b: return a
  if (a == 'i32' and b == 'f32') or (a == 'f32' and b == 'i32'): return 'i32'
  return 'i64'

Flat Lifting

The lift_flat function defines how to convert zero or more core values into a single high-level value of type t. The values are given by a value iterator that iterates over a complete parameter or result list and asserts that the expected and actual types line up. Presenting the definition of lift_flat piecewise, we start with the top-level case analysis:

@dataclass
class Value:
  t: str # 'i32'|'i64'|'f32'|'f64'
  v: int|float

@dataclass
class ValueIter:
  values: [Value]
  i = 0
  def next(self, t):
    v = self.values[self.i]
    self.i += 1
    assert(v.t == t)
    return v.v

def lift_flat(opts, vi, t):
  match despecialize(t):
    case Bool()         : return convert_int_to_bool(vi.next('i32'))
    case U8()           : return lift_flat_unsigned(vi, 32, 8)
    case U16()          : return lift_flat_unsigned(vi, 32, 16)
    case U32()          : return lift_flat_unsigned(vi, 32, 32)
    case U64()          : return lift_flat_unsigned(vi, 64, 64)
    case S8()           : return lift_flat_signed(vi, 32, 8)
    case S16()          : return lift_flat_signed(vi, 32, 16)
    case S32()          : return lift_flat_signed(vi, 32, 32)
    case S64()          : return lift_flat_signed(vi, 64, 64)
    case Float32()      : return canonicalize32(vi.next('f32'))
    case Float64()      : return canonicalize64(vi.next('f64'))
    case Char()         : return i32_to_char(opts, vi.next('i32'))
    case String()       : return lift_flat_string(opts, vi)
    case List(t)        : return lift_flat_list(opts, vi, t)
    case Record(fields) : return lift_flat_record(opts, vi, fields)
    case Variant(cases) : return lift_flat_variant(opts, vi, cases)
    case Flags(labels)  : return lift_flat_flags(vi, labels)

Integers are lifted from core i32 or i64 values using the signedness of the target type to interpret the high-order bit. When the target type is narrower than an i32, the Canonical ABI ignores the unused high bits (like load_int). The conversion logic here assumes that i32 values are always represented as unsigned Python ints and thus lifting to a signed type performs a manual 2s complement conversion in the Python (which would be a no-op in hardware).

def lift_flat_unsigned(vi, core_width, t_width):
  i = vi.next('i' + str(core_width))
  assert(0 <= i < (1 << core_width))
  return i % (1 << t_width)

def lift_flat_signed(vi, core_width, t_width):
  i = vi.next('i' + str(core_width))
  assert(0 <= i < (1 << core_width))
  i %= (1 << t_width)
  if i >= (1 << (t_width - 1)):
    return i - (1 << t_width)
  return i

The contents of strings and lists are always stored in memory so lifting these types is essentially the same as loading them from memory; the only difference is that the pointer and length come from i32 values instead of from linear memory:

def lift_flat_string(opts, vi):
  ptr = vi.next('i32')
  packed_length = vi.next('i32')
  return load_string_from_range(opts, ptr, packed_length)

def lift_flat_list(opts, vi, elem_type):
  ptr = vi.next('i32')
  length = vi.next('i32')
  return load_list_from_range(opts, ptr, length, elem_type)

Records are lifted by recursively lifting their fields:

def lift_flat_record(opts, vi, fields):
  record = {}
  for f in fields:
    record[f.label] = lift_flat(opts, vi, f.t)
  return record

Variants are also lifted recursively. Lifting a variant must carefully follow the definition of flatten_variant above, consuming the exact same core types regardless of the dynamic case payload being lifted. Because of the join performed by flatten_variant, we need a more-permissive value iterator that reinterprets between the different types appropriately and also traps if the high bits of an i64 are set for a 32-bit type:

def lift_flat_variant(opts, vi, cases):
  flat_types = flatten_variant(cases)
  assert(flat_types.pop(0) == 'i32')
  case_index = vi.next('i32')
  trap_if(case_index >= len(cases))
  class CoerceValueIter:
    def next(self, want):
      have = flat_types.pop(0)
      x = vi.next(have)
      match (have, want):
        case ('i32', 'f32') : return reinterpret_i32_as_float(x)
        case ('i64', 'i32') : return wrap_i64_to_i32(x)
        case ('i64', 'f32') : return reinterpret_i32_as_float(wrap_i64_to_i32(x))
        case ('i64', 'f64') : return reinterpret_i64_as_float(x)
        case _              : return x
  c = cases[case_index]
  if c.t is None:
    v = None
  else:
    v = lift_flat(opts, CoerceValueIter(), c.t)
  for have in flat_types:
    _ = vi.next(have)
  return { case_label_with_refinements(c, cases): v }

def wrap_i64_to_i32(i):
  assert(0 <= i < (1 << 64))
  return i % (1 << 32)

Finally, flags are lifted by OR-ing together all the flattened i32 values and then lifting to a record the same way as when loading flags from linear memory.

def lift_flat_flags(vi, labels):
  i = 0
  shift = 0
  for _ in range(num_i32_flags(labels)):
    i |= (vi.next('i32') << shift)
    shift += 32
  return unpack_flags_from_int(i, labels)

Flat Lowering

The lower_flat function defines how to convert a value v of a given type t into zero or more core values. Presenting the definition of lower_flat piecewise, we start with the top-level case analysis:

def lower_flat(opts, v, t):
  match despecialize(t):
    case Bool()         : return [Value('i32', int(v))]
    case U8()           : return [Value('i32', v)]
    case U16()          : return [Value('i32', v)]
    case U32()          : return [Value('i32', v)]
    case U64()          : return [Value('i64', v)]
    case S8()           : return lower_flat_signed(v, 32)
    case S16()          : return lower_flat_signed(v, 32)
    case S32()          : return lower_flat_signed(v, 32)
    case S64()          : return lower_flat_signed(v, 64)
    case Float32()      : return [Value('f32', canonicalize32(v))]
    case Float64()      : return [Value('f64', canonicalize64(v))]
    case Char()         : return [Value('i32', char_to_i32(v))]
    case String()       : return lower_flat_string(opts, v)
    case List(t)        : return lower_flat_list(opts, v, t)
    case Record(fields) : return lower_flat_record(opts, v, fields)
    case Variant(cases) : return lower_flat_variant(opts, v, cases)
    case Flags(labels)  : return lower_flat_flags(v, labels)

Since component-level values are assumed in-range and, as previously stated, core i32 values are always internally represented as unsigned ints, unsigned integer values need no extra conversion. Signed integer values are converted to unsigned core i32s by 2s complement arithmetic (which again would be a no-op in hardware):

def lower_flat_signed(i, core_bits):
  if i < 0:
    i += (1 << core_bits)
  return [Value('i' + str(core_bits), i)]

Since strings and lists are stored in linear memory, lifting can reuse the previous definitions; only the resulting pointers are returned differently (as i32 values instead of as a pair in linear memory):

def lower_flat_string(opts, v):
  ptr, packed_length = store_string_into_range(opts, v)
  return [Value('i32', ptr), Value('i32', packed_length)]

def lower_flat_list(opts, v, elem_type):
  (ptr, length) = store_list_into_range(opts, v, elem_type)
  return [Value('i32', ptr), Value('i32', length)]

Records are lowered by recursively lowering their fields:

def lower_flat_record(opts, v, fields):
  flat = []
  for f in fields:
    flat += lower_flat(opts, v[f.label], f.t)
  return flat

Variants are also lowered recursively. Symmetric to lift_flat_variant above, lower_flat_variant must consume all flattened types of flatten_variant, manually coercing the otherwise-incompatible type pairings allowed by join:

def lower_flat_variant(opts, v, cases):
  case_index, case_value = match_case(v, cases)
  flat_types = flatten_variant(cases)
  assert(flat_types.pop(0) == 'i32')
  c = cases[case_index]
  if c.t is None:
    payload = []
  else:
    payload = lower_flat(opts, case_value, c.t)
  for i,have in enumerate(payload):
    want = flat_types.pop(0)
    match (have.t, want):
      case ('f32', 'i32') : payload[i] = Value('i32', reinterpret_float_as_i32(have.v))
      case ('i32', 'i64') : payload[i] = Value('i64', have.v)
      case ('f32', 'i64') : payload[i] = Value('i64', reinterpret_float_as_i32(have.v))
      case ('f64', 'i64') : payload[i] = Value('i64', reinterpret_float_as_i64(have.v))
      case _              : pass
  for want in flat_types:
    payload.append(Value(want, 0))
  return [Value('i32', case_index)] + payload

Finally, flags are lowered by slicing the bit vector into i32 chunks:

def lower_flat_flags(v, labels):
  i = pack_flags_into_int(v, labels)
  flat = []
  for _ in range(num_i32_flags(labels)):
    flat.append(Value('i32', i & 0xffffffff))
    i >>= 32
  assert(i == 0)
  return flat

Lifting and Lowering Values

The lift_values function defines how to lift a list of at most max_flat core parameters or results given by the ValueIter vi into a tuple of values with types ts:

def lift_values(opts, max_flat, vi, ts):
  flat_types = flatten_types(ts)
  if len(flat_types) > max_flat:
    ptr = vi.next('i32')
    tuple_type = Tuple(ts)
    trap_if(ptr != align_to(ptr, alignment(tuple_type)))
    trap_if(ptr + size(tuple_type) > len(opts.memory))
    return list(load(opts, ptr, tuple_type).values())
  else:
    return [ lift_flat(opts, vi, t) for t in ts ]

The lower_values function defines how to lower a list of component-level values vs of types ts into a list of at most max_flat core values. As already described for flatten above, lowering handles the greater-than-max_flat case by either allocating storage with realloc or accepting a caller-allocated buffer as an out-param:

def lower_values(opts, max_flat, vs, ts, out_param = None):
  flat_types = flatten_types(ts)
  if len(flat_types) > max_flat:
    tuple_type = Tuple(ts)
    tuple_value = {str(i): v for i,v in enumerate(vs)}
    if out_param is None:
      ptr = opts.realloc(0, 0, alignment(tuple_type), size(tuple_type))
    else:
      ptr = out_param.next('i32')
    trap_if(ptr != align_to(ptr, alignment(tuple_type)))
    trap_if(ptr + size(tuple_type) > len(opts.memory))
    store(opts, tuple_value, tuple_type, ptr)
    return [ Value('i32', ptr) ]
  else:
    flat_vals = []
    for i in range(len(vs)):
      flat_vals += lower_flat(opts, vs[i], ts[i])
    return flat_vals

Canonical Definitions

Using the above supporting definitions, we can describe the static and dynamic semantics of component-level canon definitions, which have the following AST (copied from the explainer):

canon     ::= (canon lift <functype> <canonopt>* <core:funcidx> (func <id>?))
            | (canon lower <canonopt>* <funcidx> (core func <id>?))

The following subsections cover each of these cases (which will soon be extended to include async and resource/handle built-ins).

`canon lift`

For a function:

(canon lift $ft:<functype> $opts:<canonopt>* $callee:<funcidx> (func $f))

validation specifies:

$callee must have type flatten($ft, 'lift')
$f is given type $ft
a memory is present if required by lifting and is a subtype of (memory 1)
a realloc is present if required by lifting and has type (func (param i32 i32 i32 i32) (result i32))
if a post-return is present, it has type (func (param flatten($ft)['results']))

When instantiating component instance $inst:

Define $f to be the closure lambda args: canon_lift($opts, $inst, $callee, $ft, args)

Thus, $f captures $opts, $inst, $callee and $ft in a closure which can be subsequently exported or passed into a child instance (via with). If $f ends up being called by the host, the host is responsible for, in a host-defined manner, conjuring up component values suitable for passing into lower and, conversely, consuming the component values produced by lift. For example, if the host is a native JS runtime, the JavaScript embedding would specify how native JavaScript values are converted to and from component values. Alternatively, if the host is a Unix CLI that invokes component exports directly from the command line, the CLI could choose to automatically parse argv into component-level values according to the declared types of the export. In any case, canon lift specifies how these variously-produced values are consumed as parameters (and produced as results) by a single host-agnostic component.

The $inst captured above is assumed to have at least the following two fields, which are used to implement the component invariants:

class Instance:
  may_leave = True
  may_enter = True
  # ...

The may_leave state indicates whether the instance may call out to an import and the may_enter state indicates whether the instance may be called from the outside world through an export.

Given the above closure arguments, canon_lift is defined:

def canon_lift(callee_opts, callee_instance, callee, ft, args, called_as_export):
  if called_as_export:
    trap_if(not callee_instance.may_enter)
    callee_instance.may_enter = False
  else:
    assert(not callee_instance.may_enter)

  assert(callee_instance.may_leave)
  callee_instance.may_leave = False
  flat_args = lower_values(callee_opts, MAX_FLAT_PARAMS, args, ft.param_types())
  callee_instance.may_leave = True

  try:
    flat_results = callee(flat_args)
  except CoreWebAssemblyException:
    trap()

  results = lift_values(callee_opts, MAX_FLAT_RESULTS, ValueIter(flat_results), ft.result_types())
  def post_return():
    if callee_opts.post_return is not None:
      callee_opts.post_return(flat_results)
    if called_as_export:
      callee_instance.may_enter = True

  return (results, post_return)

There are a number of things to note about this definition:

Uncaught Core WebAssembly exceptions result in a trap at component boundaries. Thus, if a component wishes to signal an error, it must use some sort of explicit type such as result (whose error case particular language bindings may choose to map to and from exceptions).

The called_as_export parameter indicates whether canon_lift is being called as part of a component export or whether this canon_lift is being called internally (for example, by a child component instance). By clearing may_enter for the duration of canon_lift when called as an export, the dynamic traps ensure that components cannot be reentered, which is a [component invariant]. Furthermore, because may_enter is not cleared on the exceptional exit path taken by trap(), if there is a trap during Core WebAssembly execution or lifting/lowering, the component is left permanently un-enterable, ensuring the lockdown-after-trap [component invariant].

The contract assumed by canon_lift (and ensured by canon_lower below) is that the caller of canon_lift must call post_return right after lowering result. This ensures that post_return can be used to perform cleanup actions after the lowering is complete.

`canon lower`

For a function:

(canon lower $opts:<canonopt>* $callee:<funcidx> (core func $f))

where $callee has type $ft, validation specifies:

$f is given type flatten($ft, 'lower')
a memory is present if required by lifting and is a subtype of (memory 1)
a realloc is present if required by lifting and has type (func (param i32 i32 i32 i32) (result i32))
there is no post-return in $opts

When instantiating component instance $inst:

Define $f to be the closure: lambda args: canon_lower($opts, $inst, $callee, $ft, args)

Thus, from the perspective of Core WebAssembly, $f is a function instance containing a hostfunc that closes over $opts, $inst, $callee and $ft and, when called from Core WebAssembly code, calls canon_lower, which is defined as:

def canon_lower(caller_opts, caller_instance, callee, ft, flat_args):
  trap_if(not caller_instance.may_leave)

  flat_args = ValueIter(flat_args)
  args = lift_values(caller_opts, MAX_FLAT_PARAMS, flat_args, ft.param_types())

  results, post_return = callee(args)

  caller_instance.may_leave = False
  flat_results = lower_values(caller_opts, MAX_FLAT_RESULTS, results, ft.result_types(), flat_args)
  caller_instance.may_leave = True

  post_return()

  return flat_results

The definitions of canon_lift and canon_lower are mostly symmetric (swapping lifting and lowering), with a few exceptions:

The caller does not need a post-return function since the Core WebAssembly caller simply regains control when canon_lower returns, allowing it to free (or not) any memory passed as flat_args.
When handling the too-many-flat-values case, instead of relying on realloc, the caller pass in a pointer to caller-allocated memory as a final i32 parameter.

Since any cross-component call necessarily transits through a statically-known canon_lower+canon_lift call pair, an AOT compiler can fuse canon_lift and canon_lower into a single, efficient trampoline. This allows efficient compilation of the permissive subtyping allowed between components (including the elimination of string operations on the labels of records and variants) as well as post-MVP adapter functions.

The may_leave flag set during lowering in canon_lift and canon_lower ensures that the relative ordering of the side effects of lift and lower cannot be observed via import calls and thus an implementation may reliably interleave lift and lower whenever making a cross-component call to avoid the intermediate copy performed by lift. This unobservability of interleaving depends on the shared-nothing property of components which guarantees that all the low-level state touched by lift and lower are disjoint. Though it should be rare, same-component-instance canon_lift+canon_lower call pairs are technically allowed by the above rules (and may arise unintentionally in component reexport scenarios). Such cases can be statically distinguished by the AOT compiler as requiring an intermediate copy to implement the above lift-then-lower semantics.

Canonical ABI

The above canon definitions are parameterized, giving each component a small space of ABI options for interfacing with its contained core modules. Moreover, each component can choose its ABI options independently of each other component, with compiled adapter trampolines handling any conversions at cross-component call boundaries. However, in some contexts, it is useful to fix a single, "canonical" ABI that is fully determined by a given component type (which itself is fully determined by a set of wit files). For example, this allows existing Core WebAssembly toolchains to continue targeting WASI by importing and exporting fixed Core Module functions signatures, without having to add any new component-model concepts.

To support these use cases, the following section defines two new mappings:

canonical-module-type : componenttype -> core:moduletype
lift-canonical-module : core:module -> component

The canonical-module-type mapping defines the collection of core function signatures that a core module must import and export to implement the given component type via the Canonical ABI.

The lift-canonical-module mapping defines the runtime behavior of a core module that has successfully implemented canonical-module-type by fixing a canonical set of ABI options that are passed to the above-defined canon definitions.

Together, these definitions are intended to satisfy the invariant:

for all m : core:module and ct : componenttype:
  module-type(m) = canonical-module-type(ct) implies ct = type-of(lift-canonical-module(m))

One consequence of this is that the canonical core:moduletype must encode enough high-level type information for lift-canonical-module to be able to reconstruct a working component. This is achieved using name mangling. Unlike traditional C-family name mangling, which have a limited character set imposed by linkers and aim to be space-efficient enough to support millions of internal names, the Canonical ABI can use any valid UTF-8 string and only needs to mangle external names, of which there will only be a handful. Therefore, squeezing out every byte is a lower concern and so, for simplicity and readability, type information is mangled using a subset of the wit syntax.

One final point of note is that lift-canonical-module is only able to produce a subset of all possible components (e.g., not covering nesting and virtualization scenarios); to express the full variety of components, a toolchain needs to emit proper components directly. Thus, the Canonical ABI serves as an incremental adoption path to the full component model, allowing existing Core WebAssembly toolchains to produce simple components simply by emitting module imports and exports with the appropriate mangled names (e.g., in LLVM using the import_name and export_name attributes).

Canonical Module Type

For the same reason that core module and component binaries include a version number (that is intended to never change after it reaches 1.0), the Canonical ABI defines its own version that is explicitly declared by a core module. Before reaching stable 1.0, the Canonical ABI is explicitly allowed to make breaking changes, so this version also serves the purpose of coordinating breaking changes in pre-1.0 tools and runtimes.

CABI_VERSION = '0.1'

Working top-down, a canonical module type is defined by the following mapping:

def canonical_module_type(ct: ComponentType) -> ModuleType:
  start_params, import_funcs = mangle_instances(ct.imports)
  start_results, export_funcs = mangle_instances(ct.exports)

  imports = []
  for name,ft in import_funcs:
    flat_ft = flatten_functype(ft, 'lower')
    imports.append(CoreImportDecl('', mangle_funcname(name, ft), flat_ft))

  exports = []
  exports.append(CoreExportDecl('cabi_memory', CoreMemoryType(initial=0, maximum=None)))
  exports.append(CoreExportDecl('cabi_realloc', CoreFuncType(['i32','i32','i32','i32'], ['i32'])))

  start_ft = FuncType(start_params, start_results)
  start_name = mangle_funcname('cabi_start{cabi=' + CABI_VERSION + '}', start_ft)
  exports.append(CoreExportDecl(start_name, flatten_functype(start_ft, 'lift')))

  for name,ft in export_funcs:
    flat_ft = flatten_functype(ft, 'lift')
    exports.append(CoreExportDecl(mangle_funcname(name, ft), flat_ft))
    if any(contains_dynamic_allocation(t) for t in ft.results):
      exports.append(CoreExportDecl('cabi_post_' + name, CoreFuncType(flat_ft.results, [])))

  return ModuleType(imports, exports)

def contains_dynamic_allocation(t):
  match despecialize(t):
    case String()       : return True
    case List(t)        : return True
    case Record(fields) : return any(contains_dynamic_allocation(f.t) for f in fields)
    case Variant(cases) : return any(contains_dynamic_allocation(c.t) for c in cases)
    case _              : return False

This definition starts by mangling all nested instances into the names of the leaf fields, so that instances can be subsequently ignored. Next, each component-level function import/export is mapped to corresponding core function import/export with the function type mangled into the name. Additionally, each export whose return type implies possible dynamic allocation is given a post-return function so that it can deallocate after the caller reads the return value. Lastly, all value imports and exports are concatenated into a synthetic cabi_start function that is called immediately after instantiation.

For imports (which in Core WebAssembly are two-level), the first-level name is set to be a zero-length string so that the entire rest of the first-level string space is available for shared-everything dynamic linking.

For imports and exports, the Canonical ABI assumes that _ is not a valid character in a component-level import/export (as is currently the case in wit identifiers) and thus can safely be used to prefix auxiliary Canonical ABI-induced imports/exports.

Instance type mangling

Instance-type mangling recursively builds a dotted path string (of instance names) that is included in the mangled core import/export name:

def mangle_instances(xs, path = ''):
  values = []
  funcs = []
  for x in xs:
    name = path + x.name
    match x.t:
      case ValueType(t):
        values.append( (name, t) )
      case FuncType(params,results):
        funcs.append( (name, x.t) )
      case InstanceType(exports):
        vs,fs = mangle_instances(exports, name + '.')
        values += vs
        funcs += fs
      case TypeType(bounds):
        assert(False) # TODO: resource types
      case ComponentType(imports, exports):
        assert(False) # TODO: `canon instantiate`
      case ModuleType(imports, exports):
        assert(False) # TODO: canonical shared-everything linking
  return (values, funcs)

The three TODO cases are intended to be filled in by future PRs extending the Canonical ABI.

Function type mangling

Function types are mangled into wit-compatible syntax:

def mangle_funcname(name, ft):
  params = mangle_named_types(ft.params)
  if len(ft.results) == 1 and isinstance(ft.results[0], ValType):
    results = mangle_valtype(ft.results[0])
  else:
    results = mangle_named_types(ft.results)
  return f'{name}: func{params} -> {results}'

def mangle_named_types(nts):
  assert(all(type(nt) == tuple and len(nt) == 2 for nt in nts))
  mangled_elems = (nt[0] + ': ' + mangle_valtype(nt[1]) for nt in nts)
  return '(' + ', '.join(mangled_elems) + ')'

Value type mangling

Value types are similarly mangled into wit-compatible syntax, recursively:

def mangle_valtype(t):
  match t:
    case Bool()           : return 'bool'
    case S8()             : return 's8'
    case U8()             : return 'u8'
    case S16()            : return 's16'
    case U16()            : return 'u16'
    case S32()            : return 's32'
    case U32()            : return 'u32'
    case S64()            : return 's64'
    case U64()            : return 'u64'
    case Float32()        : return 'float32'
    case Float64()        : return 'float64'
    case Char()           : return 'char'
    case String()         : return 'string'
    case List(t)          : return 'list<' + mangle_valtype(t) + '>'
    case Record(fields)   : return mangle_recordtype(fields)
    case Tuple(ts)        : return mangle_tupletype(ts)
    case Flags(labels)    : return mangle_flags(labels)
    case Variant(cases)   : return mangle_varianttype(cases)
    case Enum(labels)     : return mangle_enumtype(labels)
    case Union(ts)        : return mangle_uniontype(ts)
    case Option(t)        : return mangle_optiontype(t)
    case Result(ok,error) : return mangle_resulttype(ok,error)

def mangle_recordtype(fields):
  mangled_fields = (f.label + ': ' + mangle_valtype(f.t) for f in fields)
  return 'record { ' + ', '.join(mangled_fields) + ' }'

def mangle_tupletype(ts):
  return 'tuple<' + ', '.join(mangle_valtype(t) for t in ts) + '>'

def mangle_flags(labels):
  return 'flags { ' + ', '.join(labels) + ' }'

def mangle_varianttype(cases):
  mangled_cases = ('{label}{payload}'.format(
                     label = c.label,
                     payload = '' if c.t is None else '(' + mangle_valtype(c.t) + ')')
                   for c in cases)
  return 'variant { ' + ', '.join(mangled_cases) + ' }'

def mangle_enumtype(labels):
  return 'enum { ' + ', '.join(labels) + ' }'

def mangle_uniontype(ts):
  return 'union { ' + ', '.join(mangle_valtype(t) for t in ts) + ' }'

def mangle_optiontype(t):
  return 'option<' + mangle_valtype(t) + '>'

def mangle_resulttype(ok, error):
  match (ok, error):
    case (None, None) : return 'result'
    case (None, _)    : return 'result<_, ' + mangle_valtype(error) + '>'
    case (_, None)    : return 'result<' + mangle_valtype(ok) + '>'
    case (_, _)       : return 'result<' + mangle_valtype(ok) + ', ' + mangle_valtype(error) + '>'

As an example, given a component type:

(component
  (import "foo" (func))
  (import "a" (instance
    (export "bar" (func (param "x" u32) (param "y" u32) (result u32)))
  ))
  (import "v1" (value string))
  (export "baz" (func (param "s" string) (result string)))
  (export "v2" (value list<list<string>>))
)

the canonical_module_type would be:

(module
  (import "" "foo: func() -> ()" (func))
  (import "" "a.bar: func(x: u32, y: u32) -> u32" (func param i32 i32) (result i32))
  (export "cabi_memory" (memory 0))
  (export "cabi_realloc" (func (param i32 i32 i32 i32) (result i32)))
  (export "cabi_start{cabi=0.1}: func(v1: string) -> (v2: list<list<string>>)" (func (param i32 i32) (result i32)))
  (export "baz: func(s: string) -> string" (func (param i32 i32) (result i32)))
  (export "cabi_post_baz" (func (param i32)))
)

Lifting Canonical Modules

TODO

class Module:
  t: ModuleType
  instantiate: Callable[typing.List[typing.Tuple[str,str,Value]], typing.List[typing.Tuple[str,Value]]]

class Component:
  t: ComponentType
  instantiate: Callable[typing.List[typing.Tuple[str,any]], typing.List[typing.Tuple[str,any]]]

def lift_canonical_module(module: Module) -> Component:
  # TODO: define component.instantiate by:
  #  1. creating canonical import adapters
  #  2. creating a core module instance that imports (1)
  #  3. creating canonical export adapters from the exports of (2)
  pass

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

CanonicalABI.md

CanonicalABI.md

Canonical ABI Explainer

Supporting definitions

Despecialization

Alignment

Size

Loading

Storing

Flattening

Flat Lifting

Flat Lowering

Lifting and Lowering Values

Canonical Definitions

`canon lift`

`canon lower`

Canonical ABI

Canonical Module Type

Instance type mangling

Function type mangling

Value type mangling

Lifting Canonical Modules

Files

CanonicalABI.md

Latest commit

History

CanonicalABI.md

File metadata and controls

Canonical ABI Explainer

Supporting definitions

Despecialization

Alignment

Size

Loading

Storing

Flattening

Flat Lifting

Flat Lowering

Lifting and Lowering Values

Canonical Definitions

canon lift

canon lower

Canonical ABI

Canonical Module Type

Instance type mangling

Function type mangling

Value type mangling

Lifting Canonical Modules

`canon lift`

`canon lower`