diff --git a/README.md b/README.md index f0870e2..51ca258 100644 --- a/README.md +++ b/README.md @@ -298,7 +298,30 @@ If you run into issues with a dltyped decorator and would like to see detailed s - In the current implementation, _every_ call will be checked, the performance overhead on most systems should be negligible (OTOO microseconds). - Pydantic default values are not checked. - Only symbolic, literal, and expressions are allowed for dimension specifiers, f-string syntax from `jaxtyping` is not supported. -- Only torch tensors and numpy arrays are supported for now. +- Only jax arrays, torch tensors and numpy arrays are supported for now. - Static shape checking is not supported, DLType only performs runtime checks, though some expression errors will be caught statically by construction if symbolic (i.e. non-string) shapes are used. - DLType does not support checkking inside unbounded container types (i.e. `list[TensorTypeBase]`) for performance reasons. - DLType does not support unions, but does support optionals. + +### A note about numpy.typing and pydantic BaseModels + +Starting with numpy 2.5.1 python 3.11 is deprecated which paves the way for use of the new PEP 695 type annotations. +These annotations replace the old TypeAlias NDArray we had before with the following internal to numpy.typing: + +[Source](https://github.com/numpy/numpy/blame/5528ef840237704d55e06992e5c528f03a15a299/numpy/_typing/_array_like.py#L15) + +``` +# implementation of numpy.typing NDArray as of numpy 2.5.1 +type NDArray[ScalarT: np.generic] = np.ndarray[_AnyShape, np.dtype[ScalarT]] +``` + +Unfortunately for us, this means that the numpy.typing module is no longer natively compatible with pydantic model fields because `type`s defined through this syntax are not compatible with `isinstance`. +See [PEP-695](https://peps.python.org/pep-0695/) for more information on why this is the case. +Furthermore, we cannot add pydantic support dynamically to the type because numpy arrays are immutable classes so monkey patching `__get_pydantic_core_schema__` is not an option. + +Because of this limitation, we recommend using plain `np.ndarray` for numpy fields in classes. +If you want to add static shapes to your tensors like you could with the `numpy.typing` module, you can do this directly on `np.ndarray` in newer versions of numpy. + +``` +array_argument: Annotated[np.ndarray[tuple[int, ...], np.dtype[np.float32 | np.float64]], dltype.FloatTensor["b c h w"]] +``` diff --git a/dltype/__init__.py b/dltype/__init__.py index c92225d..8e473ef 100644 --- a/dltype/__init__.py +++ b/dltype/__init__.py @@ -17,6 +17,7 @@ ) from dltype._lib._dtypes import SUPPORTED_TENSOR_TYPES from dltype._lib._errors import ( + DLTypeConstraintError, DLTypeDtypeError, DLTypeDuplicateError, DLTypeError, @@ -121,6 +122,7 @@ "BFloat16Tensor", "BoolTensor", "ConstantAxis", + "DLTypeConstraintError", "DLTypeDtypeError", "DLTypeDuplicateError", "DLTypeError", diff --git a/dltype/_lib/_constants.py b/dltype/_lib/_constants.py index ae6a4c8..ffa5067 100644 --- a/dltype/_lib/_constants.py +++ b/dltype/_lib/_constants.py @@ -1,5 +1,6 @@ """Constants related to the dltype library.""" +import logging import typing import warnings @@ -31,6 +32,7 @@ class _Env(BaseSettings): if DEBUG_MODE: warnings.warn("DLType debug mode enabled", UserWarning, stacklevel=1) + logging.getLogger("dltype").setLevel(logging.DEBUG) if GLOBAL_DISABLE: warnings.warn( diff --git a/dltype/_lib/_errors.py b/dltype/_lib/_errors.py index a620af1..a8b2672 100644 --- a/dltype/_lib/_errors.py +++ b/dltype/_lib/_errors.py @@ -148,3 +148,20 @@ def __init__( def __str__(self) -> str: return f"Invalid scope provider {self._bad_scope_provider}, expected 'self' or a DLTypeScopeProvider" + + +class DLTypeConstraintError(DLTypeError): + """An error raised when a constraint is violated.""" + + def __init__( + self, + tensor_name: str | None, + constraint: str | None, + error_ctx: str | None = None, + ) -> None: + self._tensor_name = tensor_name or "?" + self._constraint = constraint or "?" + super().__init__(error_ctx=error_ctx) + + def __str__(self) -> str: + return f"Constraint violation, tensor={self._tensor_name} constraint={self._constraint}" diff --git a/dltype/_lib/_parser.py b/dltype/_lib/_parser.py index 5682dbf..c680827 100644 --- a/dltype/_lib/_parser.py +++ b/dltype/_lib/_parser.py @@ -2,6 +2,7 @@ from __future__ import annotations +import contextlib import enum import math import re @@ -55,6 +56,12 @@ class _DLTypeOperator(enum.Enum): MIN = "min" MAX = "max" ISQRT = "isqrt" + EQ = "==" + GT = ">" + GE = ">=" + LT = "<" + LE = "<=" + MOD = "%" def __repr__(self) -> str: return self.value @@ -65,33 +72,55 @@ def evaluate_unary(self, a: int) -> int: return math.isqrt(a) raise NotImplementedError(self) - def evaluate(self, a: int, b: int) -> int: # noqa: PLR0911 + def evaluate(self, a: int, b: int) -> int: # noqa: C901, PLR0911, PLR0912 """Evaluate the operator.""" - if self is _DLTypeOperator.ADD: - return a + b - if self is _DLTypeOperator.SUB: - return a - b - if self is _DLTypeOperator.MUL: - return a * b - if self is _DLTypeOperator.EXP: - return int(a**b) - if self is _DLTypeOperator.DIV: - return a // b - if self is _DLTypeOperator.MIN: - return min(a, b) - if self is _DLTypeOperator.MAX: - return max(a, b) + match self: + case _DLTypeOperator.GT: + return int(a > b) + case _DLTypeOperator.GE: + return int(a >= b) + case _DLTypeOperator.LT: + return int(a < b) + case _DLTypeOperator.LE: + return int(a <= b) + case _DLTypeOperator.EQ: + return int(a == b) + case _DLTypeOperator.ADD: + return a + b + case _DLTypeOperator.SUB: + return a - b + case _DLTypeOperator.MUL: + return a * b + case _DLTypeOperator.EXP: + return int(a**b) + case _DLTypeOperator.DIV: + return a // b + case _DLTypeOperator.MIN: + return min(a, b) + case _DLTypeOperator.MAX: + return max(a, b) + case _DLTypeOperator.MOD: + return a % b + case _DLTypeOperator.ISQRT: + msg = f"Invalid unary operator {self=}" + raise AssertionError(msg) raise NotImplementedError(self) _op_precedence: typing.Final = { + _DLTypeOperator.EQ: 0, _DLTypeOperator.ADD: 1, _DLTypeOperator.SUB: 1, _DLTypeOperator.MUL: 2, _DLTypeOperator.DIV: 2, + _DLTypeOperator.MOD: 2, _DLTypeOperator.EXP: 3, _DLTypeOperator.MIN: 4, _DLTypeOperator.MAX: 4, + _DLTypeOperator.GT: 5, + _DLTypeOperator.GE: 5, + _DLTypeOperator.LT: 5, + _DLTypeOperator.LE: 5, _DLTypeOperator.ISQRT: 5, _DLTypeGroupToken.LPAREN: 6, } @@ -100,12 +129,20 @@ def evaluate(self, a: int, b: int) -> int: # noqa: PLR0911 _binary_functions: typing.Final = frozenset({_DLTypeOperator.MIN, _DLTypeOperator.MAX}) _functional_operators: typing.Final = frozenset(_unary_functions.union(_binary_functions)) _infix_operators: typing.Final = frozenset( - {_DLTypeOperator.ADD, _DLTypeOperator.SUB, _DLTypeOperator.MUL, _DLTypeOperator.DIV, _DLTypeOperator.EXP} + { + _DLTypeOperator.ADD, + _DLTypeOperator.SUB, + _DLTypeOperator.MUL, + _DLTypeOperator.DIV, + _DLTypeOperator.EXP, + _DLTypeOperator.EQ, + _DLTypeOperator.GT, + _DLTypeOperator.GE, + _DLTypeOperator.LT, + _DLTypeOperator.LE, + _DLTypeOperator.MOD, + } ) -_valid_operators: frozenset[str] = frozenset( - {op.value for op in _DLTypeOperator if op not in _functional_operators}, -) - _VALID_IDENTIFIER_RX: typing.Final = re.compile(r"^[a-zA-Z][a-zA-Z0-9\_]*$") @@ -130,6 +167,7 @@ def __init__( self.is_literal = not is_multiaxis_literal and all( isinstance(token, int) for token in postfix_expression ) + self.is_identifier = ( is_multiaxis_literal or is_named_multiaxis or (postfix_expression == [identifier]) ) @@ -142,6 +180,17 @@ def __init__( self.is_multiaxis_literal = is_multiaxis_literal self.is_anonymous = is_anonymous self.is_named_multiaxis = is_named_multiaxis + + if ( + not self.is_literal + and not self.is_anonymous + and not self.is_named_multiaxis + and not self.is_multiaxis_literal + ): + with contextlib.suppress(KeyError): + self.evaluate({}) + self.is_literal = True + _logger.debug( "Created new %s dimension expression %r", "multiaxis" if self.is_multiaxis_literal else "", self ) @@ -401,6 +450,24 @@ def _tokenize_string_expr( current_span += character if current_span: return_list.append(_span_to_tok(current_span) or _span_to_str_or_int(current_span)) + # maybe combine repeated tokens like "==" or ">=" into a single token + for op in _DLTypeOperator: + if len(op.value) > 1: + combined_token = op.value + for idx in range(len(return_list) - len(combined_token) + 1): + if all( + ( + ( + isinstance(return_list[idx + j], TokenT) + and typing.cast("_DLTypeOperator | _DLTypeSpecifier", return_list[idx + j]).value + ) + == combined_token[j] + ) + or (isinstance(return_list[idx + j], str) and return_list[idx + j] == combined_token[j]) + for j in range(len(combined_token)) + ): + # replace the sequence with the combined token + return_list[idx : idx + len(combined_token)] = [op] _assert_token_list_valid(return_list) return return_list @@ -447,6 +514,9 @@ def expression_from_string(expression: str) -> DLTypeDimensionExpression: # split the expression into the identifier and the expression if it has a specifier identifier = expression if _DLTypeSpecifier.EQUALS.value in expression: - identifier, expression = expression.split(_DLTypeSpecifier.EQUALS.value, maxsplit=1) + identifier, _expression = expression.split(_DLTypeSpecifier.EQUALS.value, maxsplit=1) + if not _expression.startswith(_DLTypeSpecifier.EQUALS.value): + # we had an anonymous axis like a==3 so leave it alone + expression = _expression tokenized = _tokenize_string_expr(expression) return _postfix_from_infix(identifier, tokenized) diff --git a/dltype/_lib/_tensor_type_base.py b/dltype/_lib/_tensor_type_base.py index 57d8ce5..a28b1c3 100644 --- a/dltype/_lib/_tensor_type_base.py +++ b/dltype/_lib/_tensor_type_base.py @@ -2,6 +2,7 @@ from __future__ import annotations +import contextlib import typing from pydantic_core import core_schema @@ -51,13 +52,41 @@ class TensorTypeBase: DTYPES: typing.ClassVar[tuple[_dtypes.DLtypeDtypeT, ...]] = () """The torch dtypes that this tensor type asserts to contain. (empty for any dtype).""" - def __init__(self, shape: str | None, *, optional: bool = False) -> None: + def __init__( + self, shape: str | None, constraints: set[str] | None = None, *, optional: bool = False + ) -> None: """Create a new tensor type object.""" self.multiaxis_index: int | None = None self.anonymous_multiaxis: bool = False self.multiaxis_name: str | None = None self.optional = optional self.expected_shape = self._parse_shape_string(shape) + self.constraints = ( + [_parser.expression_from_string(cond) for cond in sorted(constraints)] if constraints else [] + ) + self.axis_identifiers = tuple(dim.identifier for dim in self.expected_shape) + _fake_scope_eval = {axis.identifier: -1 for axis in self.expected_shape} + for expression in self.constraints: + if expression.is_literal: + msg = ( + f"Invalid constraint syntax {expression=}: constraints must be expressions, not a literal" + ) + raise SyntaxError(msg) + if expression.is_named_multiaxis or expression.is_anonymous or expression.is_multiaxis_literal: + msg = f"Invalid constraint syntax {expression=}: constraints cannot be multiaxis or anonymous" + raise SyntaxError(msg) + # Attempt to evaluate the expression with an empty scope, if we get anything other than a key error the expression is invalid. + with contextlib.suppress(Exception): + expression.evaluate({}) + msg = f"Invalid constraint syntax {expression=}: constraints must reference at least one axis identifier" + raise SyntaxError(msg) + + try: + assert expression.evaluate(_fake_scope_eval) in (0, 1) + except Exception as e: + msg = f"Invalid constraint syntax {expression=}: {e}" + raise SyntaxError(msg) from e + # only include literal dimensions that aren't multiaxis self._literal_dims = tuple( (idx, dim.evaluate({})) @@ -155,7 +184,7 @@ def validate_tensor( schema=core_schema.is_instance_schema(source_type), ) - def check( + def check( # noqa: C901, PLR0912 self, tensor: _dtypes.DLtypeTensorT, tensor_name: str = "anonymous", @@ -163,6 +192,7 @@ def check( """Check if the tensor matches this type.""" # Basic validation for multi-axis dimensions __tracebackhide__ = not _constants.DEBUG_MODE + scope: dict[str, int] = {} if self.multiaxis_index is not None: # Min required dimensions = expected shape length + extra dimensions - 1 (the multi-axis placeholder) @@ -203,3 +233,19 @@ def check( expected_shape=dim, actual=tensor.shape[adjusted_idx], ) + + scope[self.axis_identifiers[idx]] = tensor.shape[adjusted_idx] + + if self.constraints: + for idx, dim in enumerate(self.expected_shape): + # Adjust index if multiaxis exists and is before this dimension + adjusted_idx = idx + if self.multiaxis_index is not None and idx > self.multiaxis_index: + adjusted_idx += len(tensor.shape) - len(self.expected_shape) + + if dim.identifier not in scope: + scope[dim.identifier] = tensor.shape[adjusted_idx] + + for expression in self.constraints: + if not expression.evaluate(scope) == 1: + raise _errors.DLTypeConstraintError(tensor_name=tensor_name, constraint=str(expression)) diff --git a/dltype/tests/dltype_test.py b/dltype/tests/dltype_test.py index 9d8ba99..75b89fc 100644 --- a/dltype/tests/dltype_test.py +++ b/dltype/tests/dltype_test.py @@ -14,7 +14,6 @@ import jax import numpy as np -import numpy.typing as npt import pytest import torch from pydantic import BaseModel @@ -29,8 +28,8 @@ pytestmark = pytest.mark.filterwarnings("error") np_rand = np.random.RandomState(42).rand -NPFloatArrayT: TypeAlias = npt.NDArray[np.float32 | np.float64] -NPIntArrayT: TypeAlias = npt.NDArray[np.int32 | np.uint16 | np.uint32 | np.uint8] +NPFloatArrayT: TypeAlias = np.ndarray[tuple[int, ...], np.dtype[np.float32 | np.float64]] +NPIntArrayT: TypeAlias = np.ndarray[tuple[int, ...], np.dtype[np.int32 | np.uint16 | np.uint32 | np.uint8]] class _RaisesInfo(NamedTuple): @@ -44,6 +43,13 @@ class _WarnsInfo(NamedTuple): warning_type: type[Warning] = UserWarning +@dltype.dltyped() +def numpy_no_numpy_typing_function( + arr: Annotated[np.ndarray, dltype.IntTensor["1 2 3"]], +) -> Annotated[np.ndarray, dltype.FloatTensor["1 2 3"]]: + return arr.astype(np.float32) + + @dltype.dltyped() def bad_function( tensor: Annotated[torch.Tensor, dltype.TensorTypeBase["b c h w"]], @@ -1340,21 +1346,21 @@ def test_improper_base_model_construction() -> None: with pytest.raises(dltype.DLTypeDtypeError, match=r"Invalid dtype"): class _BadModel(BaseModel): # pyright: ignore[reportUnusedClass] - tensor: Annotated[npt.NDArray[np.float32], dltype.IntTensor["b c h w"]] + tensor: Annotated[NPFloatArrayT, dltype.IntTensor["b c h w"]] with pytest.raises(dltype.DLTypeDtypeError, match=r"Invalid dtype"): class _BadModel2(BaseModel): # pyright: ignore[reportUnusedClass] tensor: Annotated[ - npt.NDArray[np.float32 | np.float64], + NPFloatArrayT, dltype.IntTensor["b c h w"], ] class _GoodModel(BaseModel): # pyright: ignore[reportUnusedClass] - tensor: Annotated[npt.NDArray[np.int32], dltype.IntTensor["b c h w"]] + tensor: Annotated[NPIntArrayT, dltype.IntTensor["b c h w"]] class _GoodModel2(BaseModel): # pyright: ignore[reportUnusedClass] - tensor: Annotated[npt.NDArray[np.int8 | np.int32], dltype.IntTensor["b c h w"]] + tensor: Annotated[NPIntArrayT, dltype.IntTensor["b c h w"]] class _MyClass: @@ -1787,3 +1793,61 @@ def func(arg: type) -> None: pass func(int) + + +def test_tensor_constraints() -> None: + @dltype.dltyped() + def func( + tensor: Annotated[ + torch.Tensor, dltype.FloatTensor("b c h w", constraints={"b%2==0", "c>0", "h>0", "w>0"}) + ], + *, + fail: bool = False, + ) -> Annotated[torch.Tensor, dltype.FloatTensor("b c h w", constraints={"b%2==0", "c>1", "h>2", "w>3"})]: + if fail: + return torch.rand(4, 1, 2, 3) + return tensor + + func(torch.rand(2, 2, 3, 4)) + + with pytest.raises(dltype.DLTypeConstraintError): + func(torch.rand(3, 2, 3, 4)) + + with pytest.raises(dltype.DLTypeConstraintError): + func(torch.rand(2, 0, 0, 0)) + + with pytest.raises(dltype.DLTypeConstraintError): + func(torch.rand(2, 2, 3, 4), fail=True) + + +@pytest.mark.parametrize( + "constraints", + [ + {"invalid_constraint"}, + {"b++1"}, + {"b=1"}, + {"c=>0"}, + {"b%2=0"}, + {"...==0"}, + {"*batch==123"}, + {"b=c"}, + {"1==2"}, + {"2>1"}, + ], +) +def test_bogus_tensor_constraints(constraints: set[str]) -> None: + with pytest.raises(SyntaxError): + dltype.FloatTensor("b c h w", constraints=constraints) + + +def test_numpy_func() -> None: + numpy_no_numpy_typing_function(np.ones((1, 2, 3), dtype=np.int32)) + + with pytest.raises(dltype.DLTypeDtypeError): + numpy_no_numpy_typing_function(np.ones((1, 2, 3), dtype=np.float32)) + + with pytest.raises(dltype.DLTypeNDimsError): + numpy_no_numpy_typing_function(np.ones((1, 2), dtype=np.int32)) + + with pytest.raises(dltype.DLTypeShapeError): + numpy_no_numpy_typing_function(np.ones((1, 2, 4), dtype=np.int32)) diff --git a/pyproject.toml b/pyproject.toml index 79c5d0b..83d57bc 100644 --- a/pyproject.toml 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