from __future__ import annotations
from warnings import warn
from contextlib import contextmanager
from enum import Enum
from functools import partial, wraps
import typing
from typing import Union, Callable, List, Sequence, TypeVar, Optional
import builtins
from ..runtime.jit import jit
import inspect
import os
from .._C.libtriton import ir
from . import semantic
from ._utils import TRITON_MAX_TENSOR_NUMEL, validate_block_shape, get_primitive_bitwidth
T = TypeVar('T')
TRITON_BUILTIN = "__triton_builtin__"
PropagateNan = ir.PROPAGATE_NAN
def builtin(fn: T) -> T:
"""Mark a function as a builtin."""
assert callable(fn)
@wraps(fn)
def wrapper(*args, **kwargs):
if "_builder" not in kwargs or kwargs["_builder"] is None:
print(kwargs)
raise ValueError("Did you forget to add @triton.jit ? "
"(`_builder` argument must be provided outside of JIT functions.)")
return fn(*args, **kwargs)
setattr(wrapper, TRITON_BUILTIN, True)
return wrapper
def _tensor_member_fn(fn: T) -> T:
"""Decorator that adds this free function as a member fn on class tensor.
When called as a member function on class tensor, the first argument to `fn`
is `self`, i.e. the tensor object.
If there are multiple decorators on a function, you probably want this one
to be the highest one (i.e. furthest from the function's `def`), so it's
applied last.
Unfortunately you still need to add a type stub to the body of class tensor
in order for pytype to know about it.
"""
assert callable(fn)
orig_sig = inspect.signature(fn)
has_args = len(orig_sig.parameters.keys() - {"_builder", "_generator"}) > 1
if not fn.__doc__:
fn.__doc__ = ""
fn.__doc__ += f"""
This function can also be called as a member function on :py:class:`tensor`,
as :code:`x.{fn.__name__}({"..." if has_args else ""})` instead of
:code:`{fn.__name__}(x{", ..." if has_args else ""})`.
"""
def wrapper(*args, **kwargs):
return fn(*args, **kwargs)
new_params = list(orig_sig.parameters.values())
new_params[0] = new_params[0].replace(name='self')
new_sig = orig_sig.replace(parameters=new_params)
wrapper.__signature__ = new_sig
wrapper.__doc__ = f"Forwards to :py:func:`{fn.__name__}` free function"
if is_builtin(fn):
setattr(wrapper, TRITON_BUILTIN, True)
setattr(tensor, fn.__name__, wrapper)
return fn
def _unwrap_iterable(x):
"""Returns x[0] if x has one element and x[0] is iterable."""
if len(x) == 1:
try:
iter(x[0])
return x[0]
except TypeError:
pass
return x
def is_builtin(fn) -> bool:
"""Is this a registered triton builtin function?"""
return getattr(fn, TRITON_BUILTIN, False)
@builtin
def to_tensor(x, _builder=None):
return semantic.to_tensor(x, _builder)
class const:
"""
This class is used as a type annotation to mark pointers to constant data.
The `store` function cannot be called with a pointer to const. Constness
is part of the pointer type and the usual Triton type consistency rules
apply. For example you cannot have a function that returns constant pointer
in one return statement and non-constant pointer in another.
"""
pass
class constexpr:
"""
This class is used to store a value that is known at compile-time.
"""
def __init__(self, value):
if isinstance(value, constexpr):
self.value = value.value
else:
self.value = value
def __repr__(self) -> str:
return f"constexpr[{self.value}]"
def __index__(self):
return self.value
def __add__(self, other):
return constexpr(self.value + _constexpr_to_value(other))
def __radd__(self, other):
return constexpr(_constexpr_to_value(other) + self.value)
def __sub__(self, other):
return constexpr(self.value - _constexpr_to_value(other))
def __rsub__(self, other):
return constexpr(_constexpr_to_value(other) - self.value)
def __mul__(self, other):
return constexpr(self.value * _constexpr_to_value(other))
def __mod__(self, other):
return constexpr(self.value % _constexpr_to_value(other))
def __rmul__(self, other):
return constexpr(_constexpr_to_value(other) * self.value)
def __truediv__(self, other):
return constexpr(self.value / _constexpr_to_value(other))
def __rtruediv__(self, other):
return constexpr(_constexpr_to_value(other) / self.value)
def __floordiv__(self, other):
return constexpr(self.value // _constexpr_to_value(other))
def __rfloordiv__(self, other):
return constexpr(_constexpr_to_value(other) // self.value)
def __gt__(self, other):
return constexpr(self.value > _constexpr_to_value(other))
def __rgt__(self, other):
return constexpr(_constexpr_to_value(other) > self.value)
def __ge__(self, other):
return constexpr(self.value >= _constexpr_to_value(other))
def __rge__(self, other):
return constexpr(_constexpr_to_value(other) >= self.value)
def __lt__(self, other):
return constexpr(self.value < _constexpr_to_value(other))
def __rlt__(self, other):
return constexpr(_constexpr_to_value(other) < self.value)
def __le__(self, other):
return constexpr(self.value <= _constexpr_to_value(other))
def __rle__(self, other):
return constexpr(_constexpr_to_value(other) <= self.value)
def __eq__(self, other):
return constexpr(self.value == _constexpr_to_value(other))
def __ne__(self, other):
return constexpr(self.value != _constexpr_to_value(other))
def __bool__(self):
return bool(self.value)
def __neg__(self):
return constexpr(-self.value)
def __and__(self, other):
return constexpr(self.value & _constexpr_to_value(other))
def logical_and(self, other):
return constexpr(self.value and _constexpr_to_value(other))
def __or__(self, other):
return constexpr(self.value | _constexpr_to_value(other))
def __xor__(self, other):
return constexpr(self.value ^ _constexpr_to_value(other))
def logical_or(self, other):
return constexpr(self.value or _constexpr_to_value(other))
def __pos__(self):
return constexpr(+self.value)
def __invert__(self):
return constexpr(~self.value)
def __pow__(self, other):
return constexpr(self.value**_constexpr_to_value(other))
def __rpow__(self, other):
return constexpr(_constexpr_to_value(other)**self.value)
def __rshift__(self, other):
return constexpr(self.value >> _constexpr_to_value(other))
def __lshift__(self, other):
return constexpr(self.value << _constexpr_to_value(other))
def __not__(self):
return constexpr(not self.value)
def __iter__(self):
return iter(self.value)
def __call__(self, *args, **kwds):
return self.value(*args, **kwds)
CONSTEXPR_0 = constexpr(0)
def _unwrap_if_constexpr(o):
return o.value if isinstance(o, constexpr) else o
def check_bit_width(value, shift_value):
if isinstance(value, tensor) and isinstance(shift_value, constexpr):
bitwidth = value.type.scalar.primitive_bitwidth
if shift_value.value >= bitwidth:
warn(
f"Value {shift_value.value} exceeds the maximum bitwidth ({bitwidth}) for type '{value.dtype}'. This may result in undefined behavior."
)
class dtype:
SINT_TYPES = ['int8', 'int16', 'int32', 'int64']
UINT_TYPES = ['int1', 'uint8', 'uint16', 'uint32', 'uint64']
FP_TYPES = ['fp8e4b15', 'fp8e4nv', 'fp8e4b8', 'fp8e5', 'fp8e5b16', 'fp16', 'bf16', 'fp32', 'fp64']
STANDARD_FP_TYPES = ['fp16', 'bf16', 'fp32', 'fp64']
OTHER_TYPES = ['void']
class SIGNEDNESS(Enum):
SIGNED = 0
UNSIGNED = 1
class KIND(Enum):
BOOLEAN = 0
INTEGRAL = 1
FLOATING = 2
def __init__(self, name):
name = _unwrap_if_constexpr(name)
self.name = name
assert name in dtype.SINT_TYPES + dtype.UINT_TYPES + dtype.FP_TYPES + dtype.OTHER_TYPES, name
if name in dtype.SINT_TYPES:
self.int_signedness = dtype.SIGNEDNESS.SIGNED
self.int_bitwidth = int(name.split('int')[-1])
self.primitive_bitwidth = self.int_bitwidth
elif name in dtype.UINT_TYPES:
self.int_signedness = dtype.SIGNEDNESS.UNSIGNED
self.int_bitwidth = int(name.split('int')[-1])
self.primitive_bitwidth = self.int_bitwidth
elif name in dtype.FP_TYPES:
if name == 'fp8e4b15':
self.fp_mantissa_width = 3
self.primitive_bitwidth = 8
self.exponent_bias = 15
elif name == 'fp8e4nv':
self.fp_mantissa_width = 3
self.primitive_bitwidth = 8
self.exponent_bias = 7
elif name == 'fp8e4b8':
self.fp_mantissa_width = 3
self.primitive_bitwidth = 8
self.exponent_bias = 8
elif name == 'fp8e5':
self.fp_mantissa_width = 2
self.primitive_bitwidth = 8
self.exponent_bias = 15
elif name == 'fp8e5b16':
self.fp_mantissa_width = 2
self.primitive_bitwidth = 8
self.exponent_bias = 16
elif name == 'fp16':
self.fp_mantissa_width = 10
self.primitive_bitwidth = 16
self.exponent_bias = 15
elif name == 'bf16':
self.fp_mantissa_width = 7
self.primitive_bitwidth = 16
self.exponent_bias = 127
elif name == 'fp32':
self.fp_mantissa_width = 23
self.primitive_bitwidth = 32
self.exponent_bias = 127
elif name == 'fp64':
self.fp_mantissa_width = 52
self.primitive_bitwidth = 64
self.exponent_bias = 1023
else:
raise RuntimeError(f'Unsupported floating-point type {name}')
elif name == 'void':
self.primitive_bitwidth = 0
def is_fp8(self):
return 'fp8' in self.name
def is_fp8e4nv(self):
return self.name == 'fp8e4nv'
def is_fp8e4b8(self):
return self.name == 'fp8e4b8'
def is_fp8e4b15(self):
return self.name == 'fp8e4b15'
def is_fp8e5(self):
return self.name == 'fp8e5'
def is_fp8e5b16(self):
return self.name == 'fp8e5b16'
def is_fp16(self):
return self.name == 'fp16'
def is_bf16(self):
return self.name == 'bf16'
def is_fp32(self):
return self.name == 'fp32'
def is_fp64(self):
return self.name == 'fp64'
def is_int1(self):
return self.name == 'int1'
def is_int8(self):
return self.name == 'int8'
def is_int16(self):
return self.name == 'int16'
def is_int32(self):
return self.name == 'int32'
def is_int64(self):
return self.name == 'int64'
def is_uint8(self):
return self.name == 'uint8'
def is_uint16(self):
return self.name == 'uint16'
def is_uint32(self):
return self.name == 'uint32'
def is_uint64(self):
return self.name == 'uint64'
def is_floating(self):
return self.name in dtype.FP_TYPES
def is_standard_floating(self):
return self.name in dtype.STANDARD_FP_TYPES
def is_int_signed(self):
return self.name in dtype.SINT_TYPES
def is_int_unsigned(self):
return self.name in dtype.UINT_TYPES
def is_int(self):
return self.name in dtype.SINT_TYPES + dtype.UINT_TYPES
def is_bool(self):
return self.is_int1()
def kind(self):
if self.is_bool():
return dtype.KIND.BOOLEAN
elif self.is_int():
return dtype.KIND.INTEGRAL
else:
assert self.is_floating()
return dtype.KIND.FLOATING
def get_int_max_value(self):
if self.is_int_signed():
return 2**(self.int_bitwidth - 1) - 1
if self.is_int_unsigned():
return 2**self.int_bitwidth - 1
assert False
def get_int_min_value(self):
if self.is_int_signed():
return -2**(self.int_bitwidth - 1)
if self.is_int_unsigned():
return 0
assert False
@staticmethod
def is_dtype(type_str):
return type_str in dtype.SINT_TYPES + dtype.UINT_TYPES + dtype.FP_TYPES + dtype.OTHER_TYPES
@staticmethod
def is_void():
raise RuntimeError("Not implemented")
@staticmethod
def is_block():
return False
@staticmethod
def is_ptr():
return False
@staticmethod
def is_const():
return False
def __eq__(self, other: dtype):
if not isinstance(other, dtype):
return False
return self.name == other.name
def __ne__(self, other: dtype):
return not self.__eq__(other)
def __hash__(self):
return hash((self.name, ))
@property
def scalar(self):
return self
def to_ir(self, builder: ir.builder) -> ir.type:
if self.name.startswith("fp8"):
if self.name not in builder.options.supported_fp8_dtypes:
raise ValueError(f'type {self} not supported in this architecture. '
f'The supported fp8 dtypes are {builder.options.supported_fp8_dtypes}')
if self.name in builder.options.deprecated_fp8_dtypes:
warn(f"{self.name} is deprecated in this architecture and will be removed in a future triton release")
if self.name == 'void':
return builder.get_void_ty()
elif self.name == 'int1':
return builder.get_int1_ty()
elif self.name in ('int8', 'uint8'):
return builder.get_int8_ty()
elif self.name in ('int16', 'uint16'):
return builder.get_int16_ty()
elif self.name in ('int32', 'uint32'):
return builder.get_int32_ty()
elif self.name in ('int64', 'uint64'):
return builder.get_int64_ty()
elif self.name == 'fp8e5':
return builder.get_fp8e5_ty()
elif self.name == 'fp8e5b16':
return builder.get_fp8e5b16_ty()
elif self.name == 'fp8e4nv':
return builder.get_fp8e4nv_ty()
elif self.name == 'fp8e4b8':
return builder.get_fp8e4b8_ty()
elif self.name == 'fp8e4b15':
return builder.get_fp8e4b15_ty()
elif self.name == 'fp16':
return builder.get_half_ty()
elif self.name == 'bf16':
return builder.get_bf16_ty()
elif self.name == 'fp32':
return builder.get_float_ty()
elif self.name == 'fp64':
return builder.get_double_ty()
raise ValueError(f'fail to convert {self} to ir type')
def __str__(self):
return self.name
def codegen_name(self):
if self.name.startswith("fp"):
return "float" + self.name[2:]
elif self.name.startswith("bf"):
return "bfloat" + self.name[2:]
else:
return self.name
@property
def cache_key_part(self) -> str:
"""See cache_key_part() in triton.cc."""
return self.name
def __repr__(self):
"""Output of repr needs to be an evaluatable expression"""
return f'triton.language.{self.codegen_name()}'
_DtypeClass = dtype
class pointer_type(dtype):
def __init__(self, element_ty: dtype, address_space: int = 1, const: bool = False):
element_ty = _unwrap_if_constexpr(element_ty)
if not isinstance(element_ty, dtype):
raise TypeError(f'element_ty has type `{type(element_ty).__name__}`; expected `dtype`.')
self.element_ty = element_ty
self.address_space = address_space
self.const = const
self.name = f'pointer<{element_ty}>' if not const else f'const_pointer<{element_ty}>'
def to_ir(self, builder: ir.builder) -> ir.pointer_type:
return builder.get_ptr_ty(self.element_ty.to_ir(builder), self.address_space)
def __str__(self):
return self.name
def __repr__(self):
return self.__str__()
def is_ptr(self):
return True
def is_const(self):
return self.const
def __eq__(self, other: pointer_type) -> bool:
if not isinstance(other, pointer_type):
return False
return self.element_ty == other.element_ty and self.address_space == other.address_space and self.const == other.const
def __ne__(self, other: pointer_type) -> bool:
return not self.__eq__(other)
@property
def scalar(self):
return self
class nv_tma_desc_type(pointer_type):
def __init__(self, const=True, address_space=0):
super().__init__(uint8, const=const, address_space=address_space)
self.name = 'nv_tma_desc_type'
class block_type(dtype):
def __init__(self, element_ty: dtype, shape: List):
self.element_ty = element_ty
self.shape = _unwrap_shape(shape)
if not self.shape:
raise TypeError('0d block_type is forbidden')
self.numel = validate_block_shape(self.shape)
self.name = f'<{self.shape}, {self.element_ty}>'
def to_ir(self, builder: ir.builder) -> ir.block_type:
return builder.get_block_ty(self.element_ty.to_ir(builder), self.shape)
def __str__(self):
return self.name
def __repr__(self):
return self.__str__()
def is_block(self):
return True
def get_block_shapes(self) -> List[int]:
return self.shape
def __eq__(self, other: block_type) -> bool:
if not isinstance(other, block_type):
return False
return self.element_ty == other.element_ty and self.shape == other.shape
def __ne__(self, other: block_type) -> bool:
return not self.__eq__(other)
@property
def scalar(self):
return self.element_ty
class function_type(dtype):
def __init__(self, ret_types: List[dtype], param_types: List[dtype]) -> None:
self.ret_types = ret_types
self.param_types = param_types
def __str__(self):
return f'fn ({self.param_types}) -> {self.ret_types}'
def to_ir(self, builder: ir.builder):
ir_param_types = [ty.to_ir(builder) for ty in self.param_types]
ret_types = [ret_type.to_ir(builder) for ret_type in self.ret_types]
return builder.get_function_ty(ir_param_types, ret_types)
void = dtype('void')
int1 = dtype('int1')
int8 = dtype('int8')
int16 = dtype('int16')
int32 = dtype('int32')
int64 = dtype('int64')
uint8 = dtype('uint8')
uint16 = dtype('uint16')
uint32 = dtype('uint32')
uint64 = dtype('uint64')
float8e5 = dtype('fp8e5')
float8e5b16 = dtype('fp8e5b16')
float8e4nv = dtype('fp8e4nv')
float8e4b8 = dtype('fp8e4b8')
float8e4b15 = dtype('fp8e4b15')
float16 = dtype('fp16')
bfloat16 = dtype('bf16')
float32 = dtype('fp32')
float64 = dtype('fp64')
pi32_t = pointer_type(int32)
def get_int_dtype(bitwidth: int, signed: bool) -> dtype:
if bitwidth == 1:
return int1
elif bitwidth == 8 and signed:
return int8
elif bitwidth == 8 and not signed:
return uint8
elif bitwidth == 16 and signed:
return int16
elif bitwidth == 16 and not signed:
return uint16
elif bitwidth == 32 and signed:
return int32
elif bitwidth == 32 and not signed:
return uint32
elif bitwidth == 64 and signed:
return int64
elif bitwidth == 64 and not signed:
return uint64
else:
raise ValueError(f'Unsupported bitwidth {bitwidth} and signedness {signed}')
class _value:
"""Base class of values that exist in the triton IR (i.e. not constexprs).
"""
def __init__(self, handle):
self.handle = handle
class tensor(_value):
"""Represents an N-dimensional array of values or pointers.
:code:`tensor` is the fundamental data structure in Triton programs. Most
functions in :py:mod:`triton.language` operate on and return tensors.
Most of the named member functions here are duplicates of the free functions
in :code:`triton.language`. For example, :code:`triton.language.sqrt(x)` is
equivalent to :code:`x.sqrt()`.
:code:`tensor` also defines most of the magic/dunder methods, so you can
write :code:`x+y`, :code:`x << 2`, etc.
.. rubric:: Constructors
..
For some reason Sphinx includes __init__ before printing the full table
of methods. Not what I want, but I can't figure out how to fix it. Give
it its own section so it looks intentional. :)
"""
def __init__(self, handle, type: dtype):
"""Not called by user code."""
super().__init__(handle)
self.shape = type.shape if type.is_block() else ()
self.numel = 1
for s in self.shape:
self.numel *= s
self.numel = constexpr(self.numel)
self.type = type
self.dtype = type.scalar
self.shape = [constexpr(s) for s in self.shape]
def __str__(self) -> str:
return str(self.dtype) + '[' + ', '.join(str(s) for s in self.shape) + ']'
@builtin
def __add__(self, other, _builder=None):
return add(self, other, sanitize_overflow=False, _builder=_builder)
@builtin
def __radd__(self, other, _builder=None):
return add(other, self, sanitize_overflow=False, _builder=_builder)
@builtin
def __sub__(self, other, _builder=None):
return sub(self, other, sanitize_overflow=False, _builder=_builder)
@builtin
def __rsub__(self, other, _builder=None):
return sub(other, self, sanitize_overflow=False, _builder=_builder)
@builtin
def __mul__(self, other, _builder=None):
return mul(self, other, sanitize_overflow=False, _builder=_builder)
@builtin
def __rmul__(self, other, _builder=None):
return mul(other, self, sanitize_overflow=False, _builder=_builder)
@builtin
def __truediv__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.truediv(self, other, _builder)
@builtin
def __rtruediv__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.truediv(other, self, _builder)
@builtin
def __floordiv__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.floordiv(self, other, _builder)
@builtin
def __rfloordiv__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.floordiv(other, self, _builder)
@builtin
def __mod__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.mod(self, other, _builder)
@builtin
def __rmod__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.mod(other, self, _builder)
@builtin
def __neg__(self, _builder=None):
return semantic.minus(self, _builder)
@builtin
def __invert__(self, _builder=None):
return semantic.invert(self, _builder)
@builtin
def __and__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.and_(self, other, _builder)
@builtin
def __rand__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.and_(other, self, _builder)
@builtin
def __or__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.or_(self, other, _builder)
@builtin
def __ror__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.or_(other, self, _builder)
@builtin
def __xor__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.xor_(self, other, _builder)
@builtin
def __rxor__(self, other, _builder=None):
other = _unwrap_if_constexpr(other)
return semantic.xor_(other, self, _builder)
@builtin
def __lshift__(self, other, _builder=None):
check_bit_width(self, other)
other = _unwrap_if_constexpr(other)
return semantic.shl(self, other, _builder)
@builtin
def __rlshift__(self, other, _builder=None):
check_bit_width(other, self)
other = _unwrap_if_constexpr(other)
return semantic.shl(other, self, _builder)
@builtin
def __rshift__(self, other, _builder=None):
check_bit_width(self, other)
other = _unwrap_if_constexpr(other)
if self.dtype.is_int_signed():
return semantic.ashr(self, other, _builder)
else:
return semantic.lshr(self, other, _builder)
@builtin
def __rrshift__(self, other, _builder=None):
check_bit_width(other, self)
other = _unwrap_if_constexpr(other)
if self.dtype.is_int_signed():
return semantic.ashr(other, self, _builder)
else:
return semantic.lshr(other, self, _builder)
@builtin
def __gt__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.greater_than(self, other, _builder)
@builtin
def __rgt__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.greater_than(other, self, _builder)
@builtin
def __ge__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.greater_equal(self, other, _builder)
@builtin
def __rge__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.greater_equal(other, self, _builder)
@builtin
def __lt__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.less_than(self, other, _builder)
@builtin
def __rlt__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.less_than(other, self, _builder)
@builtin
def __le__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.less_equal(self, other, _builder)
@builtin
def __rle__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.less_equal(other, self, _builder)
@builtin
def __eq__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.equal(self, other, _builder)
@builtin
def __req__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.equal(other, self, _builder)
@builtin
def __ne__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.not_equal(self, other, _builder)
@builtin
def __rne__(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.not_equal(other, self, _builder)
@builtin
def logical_and(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.logical_and(self, other, _builder)
@builtin
def logical_or(self, other, _builder=None):
other = semantic.to_tensor(other, _builder)
return semantic.logical_or(self, other, _builder)
@builtin
def __not__(self, _builder=None):
return semantic.not_(self, _builder)
@builtin
def __getitem__(self, slices, _builder=None):
if isinstance(slices, (slice, constexpr)) or slices is None:
slices = [slices]
ret = self
for dim, sl in enumerate(slices):
if sl is None or isinstance(sl, constexpr) and sl.value is None:
ret = semantic.expand_dims(ret, dim, _builder)
elif isinstance(sl, slice) and sl.start is None and sl.stop is None and sl.step is None:
pass
else:
raise ValueError(f"unsupported tensor index: {sl}")
return ret
@property
def T(self):
"""Transposes a 2D tensor."""
assert False, "Transposition must be created by the AST Visitor"
@builtin
def to(self, dtype: dtype, fp_downcast_rounding: Optional[str] = None, bitcast: bool = False, overflow_mode: Optional[str] = None, _builder=None):
"""
Alias for :py:func:`tensor.cast`.
"""
dtype = _unwrap_if_constexpr(dtype)
bitcast = _unwrap_if_constexpr(bitcast)
if bitcast:
return semantic.bitcast(self, dtype, _builder)
return semantic.cast(self, dtype, _builder, fp_downcast_rounding, overflow_mode)
def broadcast_to(self, *shape) -> tensor:
...
def trans(self, *dims) -> tensor:
...
def permute(self, *dims) -> tensor:
...
def split(self) -> tuple[tensor, tensor]:
...
def view(self, *shape) -> tensor:
...
def reshape(self, *shape) -> tensor:
...
def expand_dims(self, axis) -> tensor:
...
def cast(self, dtype, fp_downcast_rounding=None, bitcast=False) -> tensor:
...
def store(self, value, mask=None, boundary_check=(), cache_modifier="", eviction_policy="") -> tensor:
...
def advance(self, offsets) -> tensor:
...
def atomic_cas(self, cmp, val, sem=None, scope=None) -> tensor:
...
def atomic_xchg(self, val, mask=None, sem=None, scope=None) -> tensor:
...
def atomic_add(self, val, mask=None, sem=None, scope=None) -> tensor:
...
def atomic_max(self, val, mask=None, sem=None, scope=None) -> tensor:
...
def atomic_min(self, val, mask=None, sem=None, scope=None) -> tensor:
...
def atomic_and(self, val, mask=None, sem=None, scope=None) -> tensor:
...
def atomic_or(self, val, mask=None, sem=None, scope=None) -> tensor:
...
def atomic_xor(self, val, mask=None, sem=None, scope=None) -> tensor:
...
def exp(self) -> tensor:
...
def log(self) -> tensor:
...
def cos(self) -> tensor:
...
def sin(self) -> tensor:
...
def sqrt(self) -> tensor:
...
def rsqrt(self) -> tensor:
...
def abs(self) -> tensor:
...
def reduce(self, axis, combine_fn, keep_dims=False) -> tensor:
...
def associative_scan(self, axis, combine_fn, reverse=False) -> tensor:
...
def gather(self, indices, axis) -> tensor:
...
def histogram(self, num_bins) -> tensor:
...
def cdiv(self, div) -> tensor:
...
def sigmoid(self) -> tensor:
...
def softmax(self, ieee_rounding=False) -> tensor:
...
def ravel(self) -> tensor:
...
def max(self, axis=None, return_indices=False, return_indices_tie_break_left=True, keep_dims=False) -> tensor:
...
def argmax(self, axis, tie_break_left=True, keep_dims=False) -> tensor:
...
def min(self, axis=None, return_indices=False, return_indices_tie_break_left=True, keep_dims=False) -> tensor:
...
def argmin(self, axis, tie_break_left=True, keep_dims=False) -> tensor:
...
def sum(self, axis=None, keep_dims=False) -> tensor:
...
def xor_sum(self, axis=None, keep_dims=False) -> tensor:
...
def cumsum(self, axis=0, reverse=False) -> tensor:
...
def cumprod(self, axis=0, reverse=False) -> tensor:
...
def sort(self, dim: constexpr = None, descending: constexpr = CONSTEXPR_0) -> tensor:
...
def flip(self, dim=None) -> tensor:
...
def get_bool_env_var(var_name):
v = os.getenv(var_name, "0")
return v == "1" or v == "true" or v == "on"
def _constexpr_to_value(v):
if isinstance(v, constexpr):
return v.value
return v
@builtin
def program_id(axis, _builder=None):
"""
Returns the id of the current program instance along the given :code:`axis`.
:param axis: The axis of the 3D launch grid. Must be 0, 1 or 2.
:type axis: int
"""
axis = _constexpr_to_value(axis)
return semantic.program_id(axis, _builder)
@builtin
def num_programs(axis, _builder=None):
"""
Returns the number of program instances launched along the given :code:`axis`.
:param axis: The axis of the 3D launch grid. Must be 0, 1 or 2.
:type axis: int
"""
axis = _constexpr_to_value(axis)
return semantic.num_programs(axis, _builder)
@builtin
def arange(start, end, _builder=None):
start = _constexpr_to_value(start)
end = _constexpr_to_value(end)
return semantic.arange(start, end, _builder)
arange.__doc__ = f"""
Returns contiguous values within the half-open interval :code:`[start,
end)`. :code:`end - start` must be less than or equal to
:code:`TRITON_MAX_TENSOR_NUMEL = {TRITON_MAX_TENSOR_NUMEL}`
:param start: Start of the interval. Must be a power of two.
:type start: int32
:param end: End of the interval. Must be a power of two greater than
:code:`start`.
:type end: int32
"""
def _unwrap_shape(shape):
shape = _constexpr_to_value(shape)
return [_constexpr_to_value(s) for s in shape]
def _shape_check_impl(shape):
shape = _unwrap_shape(shape)
validate_block_shape(shape)
return shape
@builtin
def full(shape, value, dtype, _builder=None):
"""
Returns a tensor filled with the scalar value for the given :code:`shape` and :code:`dtype`.
:param shape: Shape of the new array, e.g., (8, 16) or (8, )
:type shape: tuple of ints
:param value: A scalar value to fill the array with
:type value: scalar
:param dtype: Data type of the new array, e.g., :code:`tl.float16`
:type dtype: tl.dtype
"""
shape = _shape_check_impl(shape)
value = _constexpr_to_value(value)
dtype = _constexpr_to_value(dtype)
return semantic.full(shape, value, dtype, _builder)
@builtin
def broadcast(input, other, _builder=None):
"""
Tries to broadcast the two given blocks to a common compatible shape.
:param input: The first input tensor.
:type input: Block
:param other: The second input tensor.
:type other: Block
"""
return semantic.broadcast_impl_value(input, other, _builder)
@_tensor_member_fn
@builtin
def broadcast_to(input, *shape, _builder=None):
"""
Tries to broadcast the given tensor to a new :code:`shape`.
:param input: The input tensor.
:type input: Block
:param shape: The desired shape.
:type shape:
:code:`shape` can be passed as a tuple or as individual parameters: ::
# These are equivalent
broadcast_to(x, (32, 32))
broadcast_to(x, 32, 32)
"""
shape = _shape_check_impl(_unwrap_iterable(shape))
return semantic.broadcast_impl_shape(input, shape, _builder)
@_tensor_member_fn
@builtin
def trans(input: tensor, *dims, _builder=None):
"""
Permutes the dimensions of a tensor.
If the parameter :code:`dims` is not specified, the function defaults to a (1,0) permutation,
effectively transposing a 2D tensor.
:param input: The input tensor.
:param dims: The desired ordering of dimensions. For example,
:code:`(2, 1, 0)` reverses the order dims in a a 3D tensor.
:code:`dims` can be passed as a tuple or as individual parameters: ::
# These are equivalent
trans(x, (2, 1, 0))
trans(x, 2, 1, 0)
:py:func:`permute` is equivalent to this function, except it doesn't
have the special case when no permutation is specified.
"""
if not dims:
dims = (1, 0)
dims = _unwrap_iterable(dims)
return semantic.permute(input, dims, _builder)
@_tensor_member_fn
@builtin
def permute(input, *dims, _builder=None):
"""
Permutes the dimensions of a tensor.
:param input: The input tensor.
:type input: Block
:param dims: The desired ordering of dimensions. For example,
:code:`(2, 1, 0)` reverses the order dims in a a 3D tensor.
:code:`dims` can be passed as a tuple or as individual parameters: ::
# These are equivalent
permute(x, (2, 1, 0))
permute(x, 2, 1, 0)
:py:func:`trans` is equivalent to this function, except when
:code:`dims` is empty, it tries to do a (1,0) permutation.
"""
dims = _unwrap_iterable(dims)
return semantic.permute(input, dims, _builder)
@builtin
def cat(input, other, can_reorder=False, _builder=None):
"""
Concatenate the given blocks
:param input: The first input tensor.
:type input: Tensor
:param other: The second input tensor.
:type other: Tensor
:param reorder: Compiler hint. If true, the compiler is
allowed to reorder elements while concatenating inputs. Only use if the
order does not matter (e.g., result is only used in reduction ops).
Current implementation of `cat` supports only can_reorder=True.
"""
return semantic.cat(input, other, can_reorder, _builder)
@builtin
def join(a, b, _builder=None):
"""
Join the given tensors in a new, minor dimension.
For example, given two tensors of shape (4,8), produces a new tensor of
shape (4,8,2). Given two scalars, returns a tensor of shape (2).
The two inputs are broadcasted to be the same shape.
If you want to join more than two elements, you can use multiple calls to
this function. This reflects the constraint in Triton that tensors must
have power-of-two sizes.
join is the inverse of split.
:param a: The first input tensor.
:type a: Tensor
:param b: The second input tensor.
:type b: Tensor
"""
return semantic.join(a, b, _builder)
@jit
def _take_first(a, b):
return a
@_tensor_member_fn
@builtin
def split(a, _builder=None, _generator=None) -> tuple[tensor, tensor]:
"""
Split a tensor in two along its last dim, which must have size 2.
For example, given a tensor of shape (4,8,2), produces two tensors of shape
(4,8). Given a tensor of shape (2), returns two scalars.
If you want to split into more than two pieces, you can use multiple calls
to this function (probably plus calling reshape). This reflects the
constraint in Triton that tensors must have power-of-two sizes.
split is the inverse of join.
:param a: The tensor to split.
:type a: Tensor
"""
was_rank_1 = len(a.shape) == 1
if was_rank_1:
a = semantic.expand_dims(a, 0, _builder)
out_lhs, out_rhs = semantic.split(a, _builder)
if was_rank_1:
out_lhs = typing.cast(tensor, reduce(out_lhs, None, _take_first, _builder=_builder, _generator=_generator))
out_rhs = typing.cast(tensor, reduce(out_rhs, None, _take_first, _builder=_builder, _generator=_generator))
return out_lhs, out_rhs
@_tensor_member_fn
@builtin
def view(input, *shape, _builder=None):
"""
Returns a tensor with the same elements as `input` but a different shape.
The order of the elements may not be preserved.
:param input: The input tensor.
:type input: Block
:param shape: The desired shape.
:code:`shape` can be passed as a tuple or as individual parameters: ::
# These are equivalent
view(x, (32, 32))
view(x, 32, 32)
"""
warn("view is deprecated, please use reshape with can_reorder being true.")
shape = _shape_check_impl(_unwrap_iterable(shape))
return semantic.reshape(input, shape, can_reorder=False, builder=_builder)
@_tensor_member_fn
@builtin
def reshape(input, *shape, can_reorder=False, _builder=None):
"""
Returns a tensor with the same number of elements as input but with the
provided shape.
:param input: The input tensor.
:type input: Block
:param shape: The new shape.
:code:`shape` can be passed as a tuple or as individual parameters: ::
# These are equivalent
reshape(x, (32, 32))
reshape(x, 32, 32)
"""
shape = _shape_check_impl(_unwrap_iterable(shape))
return semantic.reshape(input, shape, can_reorder, _builder)
def _wrap_axis(axis, ndim):
if not (-ndim <= axis < ndim):
raise ValueError(f"invalid axis {axis}. Expected {-ndim} <= axis < {ndim}")
return axis if axis >= 0 else axis + ndim
@_tensor_member_fn
@builtin
def expand_dims(input, axis, _builder=None):
"""
Expand the shape of a tensor, by inserting new length-1 dimensions.
Axis indices are with respect to the resulting tensor, so
``result.shape[axis]`` will be 1 for each axis.
:param input: The input tensor.
:type input: tl.tensor
:param axis: The indices to add new axes
:type axis: int | Sequence[int]
"""
input = semantic.to_tensor(input, _builder)
axis = _constexpr_to_value(axis)
axes = list(axis) if isinstance(axis, Sequence) else [axis]
new_ndim = len(input.shape) + len(axes)
axes = [_wrap_axis(_constexpr_to_value(d), new_ndim) for d in axes]
if len(set(axes)) != len(axes):
raise ValueError(f"expand_dims received duplicate axes, normalized axes = {axes}")
ret = input
for a in sorted(axes):
ret = semantic.expand_dims(ret, a, _builder)
return ret
@_tensor_member_fn
@builtin
def cast(input, dtype: dtype, fp_downcast_rounding: Optional[str] = None, bitcast: bool = False, _builder=None):
"""
Casts a tensor to the given :code:`dtype`.
:param dtype: The target data type.
:type dtype: tl.dtype
:param fp_downcast_rounding: The rounding mode for downcasting
floating-point values. This parameter is only used when self is a
floating-point tensor and dtype is a floating-point type with a
smaller bitwidth. Supported values are :code:`"rtne"` (round to
nearest, ties to even) and :code:`"rtz"` (round towards zero).
:type fp_downcast_rounding: str, optional
:param bitcast: If true, the tensor is bitcasted to the given
:code:`dtype`, instead of being numerically casted.
:type bitcast: bool, optional
"""
input = semantic.to_tensor(input, _builder)
if isinstance(bitcast, constexpr):
bitcast = bitcast.value
if bitcast:
return semantic.bitcast(input, dtype, _builder)
return semantic.cast(input, dtype, _builder, fp_downcast_rounding)
@builtin
def dot(input, other, acc=None, input_precision=None, allow_tf32=None, max_num_imprecise_acc=None, out_dtype=float32,
_builder=None):
"""
Returns the matrix product of two blocks.
The two blocks must both be two-dimensional or three-dimensional and have compatible inner dimensions.
For three-dimensional blocks, `tl.dot` performs the batched matrix product,
where the first dimension of each block represents the batch dimension.
:param input: The first tensor to be multiplied.
:type input: 2D or 3D tensor of scalar-type in {:code:`int8`, :code: `float8_e5m2`, :code:`float16`, :code:`bfloat16`, :code:`float32`}
:param other: The second tensor to be multiplied.
:type other: 2D or 3D tensor of scalar-type in {:code:`int8`, :code: `float8_e5m2`, :code:`float16`, :code:`bfloat16`, :code:`float32`}
:param acc: The accumulator tensor. If not None, the result is added to this tensor.
:type acc: 2D or 3D tensor of scalar-type in {:code:`float16`, :code:`float32`, :code:`int32`}
:param input_precision: How to exercise the Tensor Cores for f32 x f32. If
the device does not have Tensor Cores or the inputs are not of dtype f32,
this option is ignored. For devices that do have tensor cores, the
default precision is tf32.
:type input_precision: string. Available options for nvidia: :code:`"tf32"`, :code:`"tf32x3"`, :code:`"ieee"`. Default: :code:`"tf32"`. Avaliable options for amd: :code:`"ieee"`.
:param allow_tf32: *Deprecated.* If true, input_precision is set to "tf32".
Only one of :code:`input_precision` and :code:`allow_tf32` can be
specified (i.e. at least one must be :code:`None`).
"""
assert input_precision is None or allow_tf32 is None, "Only one of input_precision and allow_tf32 can be specified"
if input_precision is None:
supports_tf32 = _builder and "tf32" in _builder.options.allowed_dot_input_precisions
default_precision = "tf32" if (supports_tf32 and (allow_tf32 or allow_tf32 is None)) else "ieee"
if allow_tf32:
default_precision = "hf32"
input_precision = os.getenv("TRITON_F32_DEFAULT", default_precision)
else:
if input_precision == "tf32":
input_precision = "hf32"
input_precision = _constexpr_to_value(input_precision)
out_dtype = _constexpr_to_value(out_dtype)
max_num_imprecise_acc = _constexpr_to_value(max_num_imprecise_acc)
return semantic.dot(input, other, acc, input_precision, max_num_imprecise_acc, out_dtype, _builder)
@builtin
def dot_scaled(lhs, lhs_scale, lhs_format, rhs, rhs_scale, rhs_format, acc=None, out_dtype=float32, lhs_k_pack=True, rhs_k_pack=True, _builder=None):
"""
Returns the matrix product of two blocks in microscaling format.
lhs and rhs use microscaling formats described here:
https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf
:param lhs: The first tensor to be multiplied.
:type lhs: 2D tensor of f8, f6 or f4 format packed in int32 format.
:param lhs_scale: Scale factor for lhs tensor.
:type lhs_scale: ue8m0 float8 type (currently represented as an int8 tensor).
:param lhs_format: format of the lhs tensor, available formats: {:code:`e4m3`, :code: `e5m2`, :code:`e2m3`, :code:`e3m2`, :code:`e2m1`}.
:param rhs: The second tensor to be multiplied.
:type rhs: 2D tensor of f8, f6 or f4 format packed in int32 format.
:param rhs_scale: Scale factor for rhs tensor.
:type rhs_scale: ue8m0 float8 type (currently represented as an int8 tensor).
:param rhs_format: format of the rhs tensor, available formats: {:code:`e4m3`, :code: `e5m2`, :code:`e2m3`, :code:`e3m2`, :code:`e2m1`}.
:param acc: The accumulator tensor. If not None, the result is added to this tensor.
"""
out_dtype = _constexpr_to_value(out_dtype)
assert out_dtype == float32, "Only float32 is supported for out_dtype at the moment"
return semantic.dot_scaled(lhs, lhs_scale, lhs_format, rhs, rhs_scale, rhs_format, acc, out_dtype, lhs_k_pack, rhs_k_pack, _builder)
@builtin
def load(pointer, mask=None, other=None, boundary_check=(), padding_option="", cache_modifier="", eviction_policy="",
volatile=False, care_padding = True, _builder=None):
"""
Return a tensor of data whose values are loaded from memory at location defined by `pointer`:
(1) If `pointer` is a single element pointer, a scalar is be loaded. In
this case:
- `mask` and `other` must also be scalars,
- `other` is implicitly typecast to `pointer.dtype.element_ty`, and
- `boundary_check` and `padding_option` must be empty.
(2) If `pointer` is an N-dimensional tensor of pointers, an
N-dimensional tensor is loaded. In this case:
- `mask` and `other` are implicitly broadcast to `pointer.shape`,
- `other` is implicitly typecast to `pointer.dtype.element_ty`, and
- `boundary_check` and `padding_option` must be empty.
(3) If `pointer` is a block pointer defined by `make_block_ptr`, a
tensor is loaded. In this case:
- `mask` and `other` must be `None`, and
- `boundary_check` and `padding_option` can be specified to control the behavior of out-of-bound access.
:param pointer: Pointer to the data to be loaded
:type pointer: `triton.PointerType`, or block of `dtype=triton.PointerType`
:param mask: if `mask[idx]` is false, do not load the data at address `pointer[idx]`
(must be `None` with block pointers)
:type mask: Block of `triton.int1`, optional
:param other: if `mask[idx]` is false, return `other[idx]`
:type other: Block, optional
:param boundary_check: tuple of integers, indicating the dimensions which should do the boundary check
:type boundary_check: tuple of ints, optional
:param padding_option: should be one of {"", "zero", "nan"}, the padding value to use while out of bounds. "" means an undefined value.
:param cache_modifier: changes cache option in NVIDIA PTX
:type cache_modifier: str, optional, should be one of {"", "ca", "cg"}, where "ca" stands for
cache at all levels and "cg" stands for cache at global level (cache in L2 and below, not L1), see
`cache operator <https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#cache-operators>`_ for more details.
:param eviction_policy: changes eviction policy in NVIDIA PTX
:type eviction_policy: str, optional
:param volatile: changes volatile option in NVIDIA PTX
:type volatile: bool, optional
:param care_padding: represents whether user cares about padding value or not, default is True, works as below:
1. if 'other' is not None, 'care_padding' takes no effect.
2. if 'other' is None and 'care_padding' = True, loaded tensor will fill zeroes on masked places.
3. if 'other' is None and 'care_padding' = False, masked places on loaded tensor will be random values, and tl.load may have a better performence.
:type care_padding: bool, optional
"""
mask = _constexpr_to_value(mask)
other = _constexpr_to_value(other)
if mask is not None:
mask = semantic.to_tensor(mask, _builder)
if other is not None:
other = semantic.to_tensor(other, _builder)
padding_option = _constexpr_to_value(padding_option)
cache_modifier = _constexpr_to_value(cache_modifier)
eviction_policy = _constexpr_to_value(eviction_policy)
volatile = _constexpr_to_value(volatile)
care_padding = _constexpr_to_value(care_padding)
return semantic.load(pointer, mask, other, boundary_check, padding_option, cache_modifier, eviction_policy,
volatile, care_padding, _builder)
@builtin
def _experimental_descriptor_load(desc_pointer, offsets, shape, dtype, _builder=None):
"""
Experimental feature to access TMA descriptors loads. This is an escape hatch to easily exercise TTGIR operations.
This will be removed in the future and shouldn't be used in production code.
This loads a tensor of data based on the descriptor and offsets.
"""
type = block_type(_constexpr_to_value(dtype), shape)
return semantic.descriptor_load(desc_pointer, offsets, "", "", type, _builder)
@builtin
def _experimental_descriptor_store(desc_pointer, value, offsets, _builder=None):
"""
Experimental feature to access TMA descriptors stores. This is an escape hatch to easily exercise TTGIR operations.
This will be removed in the future and shouldn't be used in production code.
This stores a tensor of data based on the descriptor and offsets.
"""
return semantic.descriptor_store(desc_pointer, value, offsets, _builder)
@_tensor_member_fn
@builtin
def store(pointer, value, mask=None, boundary_check=(), cache_modifier="", eviction_policy="", _builder=None):
"""
Store a tensor of data into memory locations defined by `pointer`.
(1) If `pointer` is a single element pointer, a scalar is stored. In
this case:
- `mask` must also be scalar, and
- `boundary_check` and `padding_option` must be empty.
(2) If `pointer` is an N-dimensional tensor of pointers, an
N-dimensional block is stored. In this case:
- `mask` is implicitly broadcast to `pointer.shape`, and
- `boundary_check` must be empty.
(3) If `pointer` is a block pointer defined by `make_block_ptr`, a block
of data is stored. In this case:
- `mask` must be None, and
- `boundary_check` can be specified to control the behavior of out-of-bound access.
`value` is implicitly broadcast to `pointer.shape` and typecast to `pointer.dtype.element_ty`.
:param pointer: The memory location where the elements of `value` are stored
:type pointer: `triton.PointerType`, or block of `dtype=triton.PointerType`
:param value: The tensor of elements to be stored
:type value: Block
:param mask: If `mask[idx]` is false, do not store `value[idx]` at `pointer[idx]`
:type mask: Block of triton.int1, optional
:param boundary_check: tuple of integers, indicating the dimensions which should do the boundary check
:type boundary_check: tuple of ints, optional
:param cache_modifier: changes cache option in NVIDIA PTX
:type cache_modifier: str, optional, should be one of {"", ".wb", ".cg", ".cs", ".wt"}, where ".wb" stands for
cache write-back all coherent levels, ".cg" stands for cache global, ".cs" stands for cache streaming, ".wt"
stands for cache write-through, see `cache operator <https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#cache-operators>`_ for more details.
:param eviction_policy: changes eviction policy in NVIDIA PTX
:type eviction_policy: str, optional, should be one of {"", "evict_first", "evict_last"}
"""
value = semantic.to_tensor(value, _builder)
mask = _constexpr_to_value(mask)
if mask is not None:
mask = semantic.to_tensor(mask, _builder)
cache_modifier = _constexpr_to_value(cache_modifier)
eviction_policy = _constexpr_to_value(eviction_policy)
return semantic.store(pointer, value, mask, boundary_check, cache_modifier, eviction_policy, _builder)
@builtin
def make_block_ptr(base: tensor, shape, strides, offsets, block_shape, order, _builder=None):
"""
Returns a pointer to a block in a parent tensor
:param base: The base pointer to the parent tensor
:param shape: The shape of the parent tensor
:param strides: The strides of the parent tensor
:param offsets: The offsets to the block
:param block_shape: The shape of the block
:param order: The order of the original data format
"""
return semantic.make_block_ptr(base, shape, strides, offsets, block_shape, order, _builder)
@_tensor_member_fn
@builtin
def advance(base, offsets, _builder=None):
"""
Advance a block pointer
:param base: the block pointer to advance
:param offsets: the offsets to advance, a tuple by dimension
"""
return semantic.advance(base, offsets, _builder)
def _add_atomic_docstr(name: str, has_cmp: bool = False) -> Callable[[T], T]:
def _decorator(func: T) -> T:
docstr = f"""
Performs an atomic {name} at the memory location specified by :code:`pointer`.
Return the data stored at :code:`pointer` before the atomic operation.
:param pointer: The memory locations to operate on
:type pointer: Block of dtype=triton.PointerDType"""
if has_cmp:
docstr += """
:param cmp: The values expected to be found in the atomic object
:type cmp: Block of dtype=pointer.dtype.element_ty"""
docstr += """
:param val: The values with which to perform the atomic operation
:type val: Block of dtype=pointer.dtype.element_ty
:param sem: Specifies the memory semantics for the operation. Acceptable values are "acquire",
"release", "acq_rel" (stands for "ACQUIRE_RELEASE"), and "relaxed". If not provided,
the function defaults to using "acq_rel" semantics.
:type sem: str, optional
:param scope: Defines the scope of threads that observe the synchronizing effect of the atomic operation.
Acceptable values are "gpu" (default), "cta" (cooperative thread array, thread block), or "sys" (stands for "SYSTEM"). The default value is "gpu".
:type scope: str, optional
"""
func.__doc__ = docstr
return func
return _decorator
@_tensor_member_fn
@builtin
@_add_atomic_docstr("compare-and-swap", has_cmp=True)
def atomic_cas(pointer, cmp, val, sem=None, scope=None, _builder=None):
cmp = semantic.to_tensor(cmp, _builder)
val = semantic.to_tensor(val, _builder)
sem = _constexpr_to_value(sem)
scope = _constexpr_to_value(scope)
return semantic.atomic_cas(pointer, cmp, val, sem, scope, _builder)
@_tensor_member_fn
@builtin
@_add_atomic_docstr("exchange")
def atomic_xchg(pointer, val, mask=None, sem=None, scope=None, _builder=None):
val = semantic.to_tensor(val, _builder)
sem = _constexpr_to_value(sem)
scope = _constexpr_to_value(scope)
mask = _constexpr_to_value(mask)
return semantic.atomic_xchg(pointer, val, mask, sem, scope, _builder)
@_tensor_member_fn
@builtin
@_add_atomic_docstr("add")
def atomic_add(pointer, val, mask=None, sem=None, scope=None, _builder=None):
val = semantic.to_tensor(val, _builder)
sem = _constexpr_to_value(sem)
scope = _constexpr_to_value(scope)
mask = _constexpr_to_value(mask)
return semantic.atomic_add(pointer, val, mask, sem, scope, _builder)
@_tensor_member_fn
@builtin
@_add_atomic_docstr("max")
def atomic_max(pointer, val, mask=None, sem=None, scope=None, _builder=None):
val = semantic.to_tensor(val, _builder)
sem = _constexpr_to_value(sem)
scope = _constexpr_to_value(scope)
mask = _constexpr_to_value(mask)
return semantic.atomic_max(pointer, val, mask, sem, scope, _builder)
@_tensor_member_fn
@builtin
@_add_atomic_docstr("min")
def atomic_min(pointer, val, mask=None, sem=None, scope=None, _builder=None):
val = semantic.to_tensor(val, _builder)
sem = _constexpr_to_value(sem)
scope = _constexpr_to_value(scope)
mask = _constexpr_to_value(mask)
return semantic.atomic_min(pointer, val, mask, sem, scope, _builder)
@_tensor_member_fn
@builtin
@_add_atomic_docstr("logical and")
def atomic_and(pointer, val, mask=None, sem=None, scope=None, _builder=None):
val = semantic.to_tensor(val, _builder)
sem = _constexpr_to_value(sem)
scope = _constexpr_to_value(scope)
mask = _constexpr_to_value(mask)
return semantic.atomic_and(pointer, val, mask, sem, scope, _builder)
@_tensor_member_fn
@builtin
@_add_atomic_docstr("logical or")
def atomic_or(pointer, val, mask=None, sem=None, scope=None, _builder=None):
val = semantic.to_tensor(val, _builder)
sem = _constexpr_to_value(sem)
scope = _constexpr_to_value(scope)
mask = _constexpr_to_value(mask)
return semantic.atomic_or(pointer, val, mask, sem, scope, _builder)
@_tensor_member_fn
@builtin
@_add_atomic_docstr("logical xor")
def atomic_xor(pointer, val, mask=None, sem=None, scope=None, _builder=None):
val = semantic.to_tensor(val, _builder)
sem = _constexpr_to_value(sem)
scope = _constexpr_to_value(scope)
mask = _constexpr_to_value(mask)
return semantic.atomic_xor(pointer, val, mask, sem, scope, _builder)
@builtin
def where(condition, x, y, _builder=None):
"""
Returns a tensor of elements from either :code:`x` or :code:`y`, depending on :code:`condition`.
Note that :code:`x` and :code:`y` are always evaluated regardless of the value of :code:`condition`.
If you want to avoid unintended memory operations, use the :code:`mask` arguments in `triton.load` and `triton.store` instead.
The shape of :code:`x` and :code:`y` are both broadcast to the shape of :code:`condition`.
:code:`x` and :code:`y` must have the same data type.
:param condition: When True (nonzero), yield x, otherwise yield y.
:type condition: Block of triton.bool
:param x: values selected at indices where condition is True.
:param y: values selected at indices where condition is False.
"""
condition = semantic.to_tensor(condition, _builder)
x = _unwrap_if_constexpr(x)
y = _unwrap_if_constexpr(y)
return semantic.where(condition, x, y, _builder)
@builtin
def add(x, y, sanitize_overflow: constexpr = True, _builder=None):
x = _unwrap_if_constexpr(x)
y = _unwrap_if_constexpr(y)
return semantic.add(x, y, sanitize_overflow, _builder)
@builtin
def sub(x, y, sanitize_overflow: constexpr = True, _builder=None):
x = _unwrap_if_constexpr(x)
y = _unwrap_if_constexpr(y)
return semantic.sub(x, y, sanitize_overflow, _builder)
@builtin
def mul(x, y, sanitize_overflow: constexpr = True, _builder=None):
x = _unwrap_if_constexpr(x)
y = _unwrap_if_constexpr(y)
return semantic.mul(x, y, sanitize_overflow, _builder)
@builtin
def minimum(x, y, propagate_nan: constexpr = PropagateNan.NONE, _builder=None):
"""
Computes the element-wise minimum of :code:`x` and :code:`y`.
:param x: the first input tensor
:type x: Block
:param y: the second input tensor
:type y: Block
:param propagate_nan: whether to propagate NaN values.
:type propagate_nan: tl.PropagateNan
.. seealso:: :class:`tl.PropagateNan`
"""
x = semantic.to_tensor(x, _builder)
y = semantic.to_tensor(y, _builder)
x = _promote_bfloat16_to_float32(x, _builder=_builder)
y = _promote_bfloat16_to_float32(y, _builder=_builder)
propagate_nan = _constexpr_to_value(propagate_nan)
return semantic.minimum(x, y, propagate_nan, _builder)
@builtin
def maximum(x, y, propagate_nan: constexpr = PropagateNan.NONE, _builder=None):
"""
Computes the element-wise maximum of :code:`x` and :code:`y`.
:param x: the first input tensor
:type x: Block
:param y: the second input tensor
:type y: Block
:param propagate_nan: whether to propagate NaN values.
:type propagate_nan: tl.PropagateNan
.. seealso:: :class:`tl.PropagateNan`
"""
x = semantic.to_tensor(x, _builder)
y = semantic.to_tensor(y, _builder)
x = _promote_bfloat16_to_float32(x, _builder=_builder)
y = _promote_bfloat16_to_float32(y, _builder=_builder)
propagate_nan = _constexpr_to_value(propagate_nan)
return semantic.maximum(x, y, propagate_nan, _builder)
@builtin
def clamp(x, min, max, propagate_nan: constexpr = PropagateNan.NONE, _builder=None):
"""
Clamps the input tensor :code:`x` within the range [min, max].
Behavior when :code:`min` > :code:`max` is undefined.
:param x: the input tensor
:type x: Block
:param min: the lower bound for clamping
:type min: Block
:param max: the upper bound for clamping
:type max: Block
:param propagate_nan: whether to propagate NaN values. Applies only to the :code:`x` tensor.
If either :code:`min` or :code:`max` is NaN, the result is undefined.
:type propagate_nan: tl.PropagateNan
.. seealso:: :class:`tl.PropagateNan`
"""
x = semantic.to_tensor(x, _builder)
min = semantic.to_tensor(min, _builder)
max = semantic.to_tensor(max, _builder)
x = _promote_bfloat16_to_float32(x, _builder=_builder)
min = _promote_bfloat16_to_float32(min, _builder=_builder)
max = _promote_bfloat16_to_float32(max, _builder=_builder)
propagate_nan = _constexpr_to_value(propagate_nan)
return semantic.clamp(x, min, max, propagate_nan, _builder)
def _add_reduction_docstr(name: str, return_indices_arg: str = None, tie_break_arg: str = None) -> Callable[[T], T]:
def _decorator(func: T) -> T:
docstr = """
Returns the {name} of all elements in the :code:`input` tensor along the provided :code:`axis`
:param input: the input values
:type input: Tensor
:param axis: the dimension along which the reduction should be done. If None, reduce all dimensions
:type axis: int
:param keep_dims: if true, keep the reduced dimensions with length 1
:type keep_dims: bool"""
if return_indices_arg is not None:
docstr += f"""
:param {return_indices_arg}: if true, return index corresponding to the {name} value
:type {return_indices_arg}: bool"""
if tie_break_arg is not None:
docstr += f"""
:param {tie_break_arg}: if true, in case of a tie (i.e., multiple elements have the same {name} value), return the left-most index for values that aren't NaN
:type {tie_break_arg}: bool"""
func.__doc__ = docstr.format(name=name)
return func
return _decorator
@contextmanager
def _insertion_guard(builder):
ip = builder.get_insertion_point()
yield
builder.restore_insertion_point(ip)
@_tensor_member_fn
@builtin
def reduce(input, axis, combine_fn, keep_dims=False, _builder=None, _generator=None):
"""Applies the combine_fn to all elements in :code:`input` tensors along the provided :code:`axis`
:param input: the input tensor, or tuple of tensors
:type input: Tensor
:param axis: the dimension along which the reduction should be done. If None, reduce all dimensions
:type axis: int | None
:param combine_fn: a function to combine two groups of scalar tensors (must be marked with @triton.jit)
:type combine_fn: Callable
:param keep_dims: if true, keep the reduced dimensions with length 1
:type keep_dims: bool
"""
if isinstance(input, tensor):
return reduce((input, ), axis, combine_fn, keep_dims=keep_dims, _builder=_builder, _generator=_generator)[0]
def make_combine_region(reduce_op):
in_scalar_tys = [t.type.scalar for t in input]
prototype = function_type(in_scalar_tys, in_scalar_tys * 2)
region = reduce_op.get_region(0)
with _insertion_guard(_builder):
param_types = [ty.to_ir(_builder) for ty in prototype.param_types]
block = _builder.create_block_with_parent(region, param_types)
args = [tensor(block.arg(i), ty) for i, ty in enumerate(prototype.param_types)]
results = _generator.call_JitFunction(combine_fn, args, kwargs={})
if isinstance(results, tensor):
handles = [results.handle]
else:
handles = [r.handle for r in results]
_builder.create_reduce_ret(*handles)
def expand_ndims(t, ndims):
for _ in builtins.range(ndims):
t = expand_dims(t, 0, _builder=_builder)
return t
axis = _constexpr_to_value(axis)
keep_dims = _constexpr_to_value(keep_dims)
if axis is not None:
axis = _wrap_axis(axis, len(input[0].shape))
ret = semantic.reduction(input, axis, make_combine_region, _builder)
if keep_dims:
if axis is not None:
ret = builtins.tuple(expand_dims(t, axis, _builder=_builder) for t in ret)
else:
ret = builtins.tuple(expand_ndims(t, len(input[0].shape)) for t in ret)
return ret
@builtin
def _promote_bfloat16_to_float32(t, _builder=None):
scalar_ty = t.type.scalar
if scalar_ty is bfloat16:
return t.to(float32, _builder=_builder)
return t
@builtin
def _reduce_with_indices(input, axis, combine_fn, keep_dims=False, _builder=None, _generator=None):
axis = _constexpr_to_value(axis)
n = input.shape[axis]
index = arange(0, n, _builder=_builder)
if len(input.shape) > 1:
axes_to_expand = [constexpr(d) for d in builtins.range(len(input.shape))]
del axes_to_expand[axis]
index = expand_dims(index, axes_to_expand, _builder=_builder)
index = broadcast_to(index, input.shape, _builder=_builder)
rvalue, rindices = reduce((input, index), axis, combine_fn, keep_dims=keep_dims, _builder=_builder,
_generator=_generator)
return rvalue, rindices
def _add_scan_docstr(name: str) -> Callable[[T], T]:
def _decorator(func: T) -> T:
docstr = """
Returns the {name} of all elements in the :code:`input` tensor along the provided :code:`axis`
:param input: the input values
:type input: Tensor
:param axis: the dimension along which the scan should be done
:type axis: int"""
func.__doc__ = docstr.format(name=name)
return func
return _decorator
@_tensor_member_fn
@builtin
def associative_scan(input, axis, combine_fn, reverse=False, _builder=None, _generator=None):
"""Applies the combine_fn to each elements with a carry in :code:`input` tensors along the provided :code:`axis` and update the carry
:param input: the input tensor, or tuple of tensors
:type input: Tensor
:param axis: the dimension along which the reduction should be done
:type axis: int
:param combine_fn: a function to combine two groups of scalar tensors (must be marked with @triton.jit)
:type combine_fn: Callable
:param reverse: whether to apply the associative scan in the reverse direction along axis
:type reverse: bool
"""
if isinstance(input, tensor):
return associative_scan((input, ), axis, combine_fn, reverse, _builder=_builder, _generator=_generator)[0]
def make_combine_region(scan_op):
in_scalar_tys = [t.type.scalar for t in input]
prototype = function_type(in_scalar_tys, in_scalar_tys * 2)
region = scan_op.get_region(0)
with _insertion_guard(_builder):
param_types = [ty.to_ir(_builder) for ty in prototype.param_types]
block = _builder.create_block_with_parent(region, param_types)
args = [tensor(block.arg(i), ty) for i, ty in enumerate(prototype.param_types)]
results = _generator.call_JitFunction(combine_fn, args, kwargs={})
if isinstance(results, tensor):
handles = [results.handle]
else:
handles = [r.handle for r in results]
_builder.create_scan_ret(*handles)
axis = _constexpr_to_value(axis)
if axis is not None:
axis = _wrap_axis(axis, len(input[0].shape))
return semantic.associative_scan(input, axis, make_combine_region, reverse, _builder)
@_tensor_member_fn
@builtin
def histogram(input, num_bins, _builder=None, _generator=None):
"""computes an histogram based on input tensor with num_bins bins, the bins have a width of 1 and start at 0.
:param input: the input tensor
:type input: Tensor
:param num_bins: number of histogram bins
:type num_bins: int
"""
num_bins = _constexpr_to_value(num_bins)
return semantic.histogram(input, num_bins, _builder)
@_tensor_member_fn
@builtin
def gather(src, index, axis, _builder=None):
"""Gather from a tensor along a given dimension.
:param src: the source tensor
:type src: Tensor
:param index: the index tensor
:type index: Tensor
:param axis: the dimension to gather along
:type axis: int
"""
axis = _constexpr_to_value(axis)
return semantic.gather(src, index, axis, _builder)
@builtin
def debug_barrier(_builder=None):
'''
Insert a barrier to synchronize all threads in a block.
'''
return semantic.debug_barrier(_builder)
@builtin
def multiple_of(input, values, _builder=None):
"""
Let the compiler know that the values in :code:`input` are all multiples of :code:`value`.
"""
if isinstance(values, constexpr):
values = [values]
for i, d in enumerate(values):
if not isinstance(d, constexpr):
raise TypeError(f"values element {i} must have type `constexpr`")
if not isinstance(d.value, int):
raise TypeError(f"values element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]")
values = [x.value for x in values]
return semantic.multiple_of(input, values)
@builtin
def max_contiguous(input, values, _builder=None):
"""
Let the compiler know that the `value` first values in :code:`input` are contiguous.
"""
if isinstance(values, constexpr):
values = [values]
for i, d in enumerate(values):
if not isinstance(d, constexpr):
raise TypeError(f"values element {i} must have type `constexpr`")
if not isinstance(d.value, int):
raise TypeError(f"values element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]")
values = [x.value for x in values]
return semantic.max_contiguous(input, values)
@builtin
def max_constancy(input, values, _builder=None):
"""
Let the compiler know that the `value` first values in :code:`input` are constant.
e.g. if :code:`values` is [4], then each group of 4 values in :code:`input` should all be equal,
for example [0, 0, 0, 0, 1, 1, 1, 1].
"""
if isinstance(values, constexpr):
values = [values]
for i, d in enumerate(values):
if not isinstance(d, constexpr):
raise TypeError(f"values element {i} must have type `constexpr`")
if not isinstance(d.value, int):
raise TypeError(f"values element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]")
values = [x.value for x in values]
return semantic.max_constancy(input, values)
@builtin
def assume(cond, _builder=None):
'''
Allow compiler to assume the :code:`cond` is True.
'''
return semantic.assume(semantic.to_tensor(cond, _builder), _builder)
@builtin
def static_print(*values, sep: str = " ", end: str = "\n", file=None, flush=False, _builder=None):
'''
Print the values at compile time. The parameters are the same as the builtin :code:`print`.
NOTE: Calling the Python builtin :code:`print` is not the same as calling this, it instead maps to :code:`device_print`,
which has special requirements for the arguments.
.. highlight:: python
.. code-block:: python
tl.static_print(f"BLOCK_SIZE={BLOCK_SIZE}")
'''
pass
@builtin
def static_assert(cond, msg="", _builder=None):
'''
Assert the condition at compile time. Does not require that the :code:`TRITON_DEBUG` environment variable
is set.
.. highlight:: python
.. code-block:: python
tl.static_assert(BLOCK_SIZE == 1024)
'''
pass
@builtin
def device_print(prefix, *args, hex=False, _builder=None):
'''
Print the values at runtime from the device. String formatting does not work for runtime values, so you should
provide the values you want to print as arguments. The first value must be a string, all following values must
be scalars or tensors.
Calling the Python builtin :code:`print` is the same as calling this function, and the requirements for the arguments will match
this function (not the normal requirements for :code:`print`).
.. highlight:: python
.. code-block:: python
tl.device_print("pid", pid)
print("pid", pid)
On CUDA, printfs are streamed through a buffer of limited size (on one host,
we measured the default as 6912 KiB, but this may not be consistent across
GPUs and CUDA versions). If you notice some printfs are being dropped, you
can increase the buffer size by calling
.. highlight:: python
.. code-block:: python
triton.runtime.driver.active.utils.set_printf_fifo_size(size_bytes)
CUDA may raise an error if you try to change this value after running a
kernel that uses printfs. The value set here may only affect the current
device (so if you have multiple GPUs, you'd need to call it multiple times).
:param prefix: a prefix to print before the values. This is required to be a string literal.
:param args: the values to print. They can be any tensor or scalar.
:param hex: print all values as hex instead of decimal
'''
import string
prefix = _constexpr_to_value(prefix)
assert isinstance(prefix, str), f"{prefix} is not string"
b_ascii = True
for ch in prefix:
if ch not in string.printable:
b_ascii = False
break
assert b_ascii, f"{prefix} is not an ascii string"
new_args = []
for arg in args:
new_args.append(semantic.to_tensor(arg, _builder))
return semantic.device_print(prefix, new_args, hex, _builder)
@builtin
def device_assert(cond, msg="", _builder=None):
'''
Assert the condition at runtime from the device. Requires that the environment variable :code:`TRITON_DEBUG`
is set to a value besides :code:`0` in order for this to have any effect.
Using the Python :code:`assert` statement is the same as calling this function, except that the second argument
must be provided and must be a string, e.g. :code:`assert pid == 0, "pid != 0"`. The environment variable must
be set for this :code:`assert` statement to have any effect.
.. highlight:: python
.. code-block:: python
tl.device_assert(pid == 0)
assert pid == 0, f"pid != 0"
:param cond: the condition to assert. This is required to be a boolean tensor.
:param msg: the message to print if the assertion fails. This is required to be a string literal.
'''
msg = _constexpr_to_value(msg)
return semantic.device_assert(semantic.to_tensor(cond, _builder), msg, _builder)
@builtin
def inline_asm_elementwise(asm: str, constraints: str, args: Sequence, dtype: Union[dtype, Sequence[dtype]],
is_pure: bool, pack: int, _builder=None):
'''
Execute inline assembly over a tensor. Essentially, this is :code:`map`
where the function is inline assembly.
The input tensors :code:`args` are implicitly broadcasted to the same shape.
:code:`dtype` can be a tuple of types, in which case the output is a
tuple of tensors.
Each invocation of the inline asm processes :code:`pack` elements at a
time. Exactly which set of inputs a block receives is unspecified.
Input elements of size less than 4 bytes are packed into 4-byte
registers.
This op does not support empty :code:`dtype` -- the inline asm must
return at least one tensor, even if you don't need it. You can work
around this by returning a dummy tensor of arbitrary type; it shouldn't
cost you anything if you don't use it.
Example using
`PTX <https://docs.nvidia.com/cuda/parallel-thread-execution/index.html>`_
assembly:
.. highlight:: python
.. code-block:: python
@triton.jit
def kernel(A, B, C, D, BLOCK: tl.constexpr):
a = tl.load(A + tl.arange(0, BLOCK)) # uint8 tensor
b = tl.load(B + tl.arange(0, BLOCK)) # float32 tensor
# For each (a,b) in zip(a,b), perform the following:
# - Let ai be `a` converted to int32.
# - Let af be `a` converted to float.
# - Let m be the max of ai and b.
# - Return ai and mi.
# Do the above 4 elements at a time.
(c, d) = tl.inline_asm_elementwise(
asm="""
{
// Unpack `a` into `ai`.
.reg .b8 tmp<4>;
mov.b32 {tmp0, tmp1, tmp2, tmp3}, $8;
cvt.u32.u8 $0, tmp0;
cvt.u32.u8 $1, tmp1;
cvt.u32.u8 $2, tmp2;
cvt.u32.u8 $3, tmp3;
}
// Convert `ai` to float.
cvt.rn.f32.s32 $4, $0;
cvt.rn.f32.s32 $5, $1;
cvt.rn.f32.s32 $6, $2;
cvt.rn.f32.s32 $7, $3;
// Take max of `ai` and `b`.
max.f32 $4, $4, $9;
max.f32 $5, $5, $10;
max.f32 $6, $6, $11;
max.f32 $7, $7, $12;
""",
constraints=(
# 8 output registers, namely
# $0=ai0, $1=ai1, $2=ai2, $3=ai3,
# $4=m0, $5=m1, $6=m2, $7=m3.
"=r,=r,=r,=r,=r,=r,=r,=r,"
# 5 input registers, namely
# $8=ai,
# $9=b0, $10=b1, $11=b2, $12=b3.
# The four elements from `a` are all packed into one register.
"r,r,r,r,r"),
args=[a, b],
dtype=(tl.int32, tl.float32),
is_pure=True,
pack=4,
)
tl.store(C + tl.arange(0, BLOCK), c)
tl.store(D + tl.arange(0, BLOCK), d)
:param asm: assembly to run. Must match target's assembly format.
:param constraints: asm constraints in
`LLVM format <https://llvm.org/docs/LangRef.html#inline-asm-constraint-string>`_
:param args: the input tensors, whose values are passed to the asm block
:param dtype: the element type(s) of the returned tensor(s)
:param is_pure: if true, the compiler assumes the asm block has no side-effects
:param pack: the number of elements to be processed by one instance of inline assembly
:param _builder: the builder
:return: one tensor or a tuple of tensors of the given dtypes
'''
asm = _constexpr_to_value(asm)
constraints = _constexpr_to_value(constraints)
pack = _constexpr_to_value(pack)
is_pure = _constexpr_to_value(is_pure)
try:
iter(dtype)
has_multiple_outputs = True
except TypeError:
has_multiple_outputs = False
dtype = (dtype, )
dtype = typing.cast(Sequence[_DtypeClass], dtype)
res_tys = dtype
if dispatch_args := [semantic.to_tensor(arg, _builder) for arg in args]:
bin_op_type_checking = partial(
semantic.binary_op_type_checking_impl,
builder=_builder,
arithmetic_check=False,
allow_lhs_ptr=True,
allow_rhs_ptr=True,
)
broadcast_arg = dispatch_args[0]
for item in dispatch_args:
_, broadcast_arg = bin_op_type_checking(item, broadcast_arg)
if broadcast_arg.shape:
for i, item in enumerate(dispatch_args):
dispatch_args[i], _ = bin_op_type_checking(item, broadcast_arg)
res_tys = [block_type(dt, broadcast_arg.shape) for dt in dtype]
handles = [t.handle for t in dispatch_args]
call = _builder.create_inline_asm(asm, constraints, handles, [ty.to_ir(_builder) for ty in res_tys], is_pure, pack)
if not has_multiple_outputs:
return tensor(call.get_result(0), res_tys[0])
return builtins.tuple(tensor(call.get_result(i), ty) for i, ty in enumerate(res_tys))
class static_range:
"""
Iterator that counts upward forever.
.. highlight:: python
.. code-block:: python
@triton.jit
def kernel(...):
for i in tl.static_range(10):
...
:note: This is a special iterator used to implement similar semantics to Python's :code:`range` in the context of
:code:`triton.jit` functions. In addition, it also guides the compiler to unroll the loop aggressively.
:param arg1: the start value.
:param arg2: the end value.
:param step: the step value.
"""
def __init__(self, arg1, arg2=None, step=None):
assert isinstance(arg1, constexpr), f"{arg1} used as tl.static_range start value is not a constexpr"
if step is None:
self.step = constexpr(1)
else:
assert isinstance(step, constexpr), f"{step} used as tl.static_range step value is not a constexpr"
self.step = step
if arg2 is None:
self.start = constexpr(0)
self.end = arg1
else:
assert isinstance(arg2, constexpr), f"{arg2} used as tl.static_range end value is not a constexpr"
self.start = arg1
self.end = arg2
def __iter__(self):
raise RuntimeError("static_range can only be used in @triton.jit'd functions")
def __next__(self):
raise RuntimeError("static_range can only be used in @triton.jit'd functions")
class range:
"""
Iterator that counts upward forever.
.. highlight:: python
.. code-block:: python
@triton.jit
def kernel(...):
for i in tl.range(10, num_stages=3):
...
:note: This is a special iterator used to implement similar semantics to Python's :code:`range` in the context of
:code:`triton.jit` functions. In addition, it allows user to pass extra attributes to the compiler.
:param arg1: the start value.
:param arg2: the end value.
:param step: the step value.
:param num_stages: pipeline the loop into this many stages (so there are
:code:`num_stages` iterations of the loop in flight at once).
Note this is subtly different than passing :code:`num_stages` as a
kernel argument. The kernel argument only pipelines loads that feed
into :code:`dot` operations, while this attribute tries to pipeline most
(though not all) loads in this loop.
:param loop_unroll_factor: Tells the Triton IR level loop unroller how many
times to unroll a for loop that this range is used with. Less than 2 for
this value implies no unrolling.
:param disallow_acc_multi_buffer: If true, prevent the accumulator of the dot
operation in the loop to be multi-buffered, if applicable.
:param flatten: automatically flatten the loop nest starting at this loop to
create a single flattened loop. The compiler will try to pipeline the
flattened loop which can avoid stage stalling.
:param warp_specialize: Enable automatic warp specialization on the loop.
The compiler will attempt to partition memory, MMA, and vector
operations in the loop into separate async partitions. This will
increase the total number of warps required by the kernel.
:param disable_licm: Tells the compiler it shouldn't hoist loop invariant
code outside the loop. This is often useful to avoid creating long liveranges
within a loop.
Note that warp specialization is only supported on Blackwell GPUs and
only works on simple matmul loops. Support for arbitrary loops will be
expanded over time.
"""
def __init__(self, arg1, arg2=None, step=None, num_stages=None, loop_unroll_factor=None,
disallow_acc_multi_buffer=False, flatten=False, warp_specialize=False, disable_licm=False):
if step is None:
self.step = constexpr(1)
else:
self.step = step
if arg2 is None:
self.start = constexpr(0)
self.end = arg1
else:
self.start = arg1
self.end = arg2
self.num_stages = num_stages
self.loop_unroll_factor = loop_unroll_factor
self.disallow_acc_multi_buffer = disallow_acc_multi_buffer
self.flatten = flatten
self.warp_specialize = warp_specialize
self.disable_licm = disable_licm
def __iter__(self):
raise RuntimeError("tl.range can only be used in @triton.jit'd functions")
def __next__(self):
raise RuntimeError("tl.range can only be used in @triton.jit'd functions")
def dispatch(func, lib_name: str, lib_path: str, args: list, arg_type_symbol_dict: dict, ret_shape: tuple,
is_pure: bool, _builder=None):
'''
Dispatch a function to a library
:param func: the function to dispatch
:param lib_name: the name of the library
:param lib_path: the path of the library
:param args: the arguments of the function
:param arg_type_symbol_dict: the type of the arguments
:param ret_shape: the shape of the return value
:param _builder: the builder
:return: the return value of the function
'''
if len(arg_type_symbol_dict) == 0:
raise ValueError("arg_type_symbol_dict is empty")
num_args = len(list(arg_type_symbol_dict.keys())[0])
if len(args) != num_args:
raise ValueError(f"length of input args does not match."
f"Expect {len(args)}, got {num_args}")
arg_types = []
arg_list = []
for arg in args:
if isinstance(arg, tensor):
arg_types.append(arg.dtype)
arg_list.append(arg.handle)
else:
arg_types.append(type(arg))
arg_list.append(arg)
arg_types = tuple(arg_types)
if arg_types not in arg_type_symbol_dict:
raise ValueError(f"input arg type does not match."
f"Expect one of {arg_type_symbol_dict.keys()}, got {arg_types}")
else:
symbol = arg_type_symbol_dict[arg_types][0]
ret_type = arg_type_symbol_dict[arg_types][1]
if ret_shape:
ret_type = block_type(ret_type, ret_shape)
return tensor(func(lib_name, lib_path, symbol, arg_list, ret_type.to_ir(_builder), is_pure), ret_type)
@builtin
def extern_elementwise(lib_name: str, lib_path: str, args: list, arg_type_symbol_dict: dict, is_pure: bool,
_builder=None):
'''
Dispatch an elementwise function to a library
:param lib_name: the name of the library
:param lib_path: the path of the library
:param args: the arguments of the function
:param arg_type_symbol_dict: the type of the arguments
:param is_pure: whether the function is pure
:param _builder: the builder
:return: the return value of the function
'''
dispatch_args = args.copy()
all_scalar = True
ret_shape = None
arg_types = []
for i in builtins.range(len(dispatch_args)):
dispatch_args[i] = semantic.to_tensor(dispatch_args[i], _builder)
arg_types.append(dispatch_args[i].dtype)
if dispatch_args[i].type.is_block():
all_scalar = False
if len(arg_types) > 0:
arg_types = tuple(arg_types)
arithmetic_check = True
if arg_types in arg_type_symbol_dict:
arithmetic_check = False
broadcast_arg = dispatch_args[0]
for item in dispatch_args:
_, broadcast_arg = semantic.binary_op_type_checking_impl(item, broadcast_arg, _builder,
arithmetic_check=arithmetic_check)
for i in builtins.range(len(dispatch_args)):
dispatch_args[i], _ = semantic.binary_op_type_checking_impl(dispatch_args[i], broadcast_arg, _builder,
arithmetic_check=arithmetic_check)
if not all_scalar:
ret_shape = broadcast_arg.shape
func = _builder.create_extern_elementwise
return dispatch(func, lib_name, lib_path, dispatch_args, arg_type_symbol_dict, ret_shape, is_pure, _builder)
def binary_op_type_legalization(lhs, rhs, builder):
'''
Convert both operands to a single common type
:param lhs: the left operand
:param rhs: the right operand
:param builder: the builder
'''
return semantic.binary_op_type_checking_impl(lhs, rhs, builder)
def extern(fn):
"""A decorator for external functions."""
return builtin(fn)
def _unwrap_if_constexpr_3_4(o):
if isinstance(o, list):
return [_unwrap_if_constexpr_3_4(x) for x in o]
if isinstance(o, builtins.tuple):
return builtins.tuple(_unwrap_if_constexpr_3_4(x) for x in o)
if isinstance(o, tuple):
return tuple(_unwrap_if_constexpr_3_4(x) for x in o)
return o.value if isinstance(o, constexpr) else o
def _unwrap_shape_3_4(shape):
shape = _unwrap_if_constexpr_3_4(shape)
return [_unwrap_if_constexpr_3_4(s) for s in shape]
def _normalize_tuple(t):
normalized_tuple = _unwrap_if_constexpr_3_4(t)
if isinstance(normalized_tuple, (list, builtins.tuple)):
normalized_tuple = tuple(normalized_tuple)
return normalized_tuple
class base_value(_value):
"""Base class of values that exist in the triton IR (i.e. not constexprs).
"""
type: base_type
def _flatten_ir(self, handles: List[ir.value]) -> None:
"""Flatten frontend value into a sequence of mlir handles, which are appended
to the output list
"""
raise NotImplementedError
class base_type:
def __eq__(self, other):
raise NotImplementedError("Types must implement __eq__")
def __ne__(self, other):
return not (self == other)
def _unflatten_ir(self, handles: List[ir.value], cursor: int) -> Tuple[base_value, int]:
"""Build a frontend value with the current dtype, wrapping a list of existing handles.
cursor is the index of the first handle relevant to this value, and the function
should return the updated cursor position after any handles consumed by the created value.
"""
raise NotImplementedError
def mangle(self) -> str:
raise NotImplementedError(f"NYI: Type mangling for type {self.__class__}")
def _flatten_ir_types(self, builder: ir.builder, out: List[ir.type]) -> None:
raise NotImplementedError
class tuple(base_value):
def __init__(self, args: Sequence, type: tuple_type = None):
self.values = [i for i in args]
def get_type(x):
if isinstance(x, dtype):
return dtype
if isinstance(x, (int, float)):
return constexpr
return x.type
self.type = type or tuple_type([get_type(x) for x in self.values])
def __getitem__(self, idx: constexpr):
if isinstance(idx, int):
idx = constexpr(idx)
if isinstance(idx, constexpr):
return self.values[idx]
else:
assert isinstance(idx, (slice, builtins.slice))
return tuple(self.values[idx.start:idx.stop:idx.step])
def __getattr__(self, name):
return self.values[self.type.fields.index(name)]
def __setitem__(self, idx: constexpr, value):
if isinstance(idx, int):
idx = constexpr(idx)
assert isinstance(idx, constexpr)
self.values[idx] = value
def __add__(self, other):
other = _normalize_tuple(other)
return tuple(self.values + other.values)
def __mul__(self, other):
assert isinstance(other, constexpr)
return tuple(self.values * other.value)
def __eq__(self, other):
other = _normalize_tuple(other)
return constexpr(self.values == other.values)
def __hash__(self):
return hash(builtins.tuple(self.values))
def __str__(self):
return str([str(x) for x in self.values])
def __iter__(self):
return iter(self.values)
def __len__(self):
return len(self.values)
def _flatten_ir(self, handles: List[ir.value]):
for v in self.values:
print("[debug]tuple _flatten_ir: value:", v)
v._flatten_ir(handles)
print("[debug]tuple _flatten_ir: handles:", handles)
def __repr__(self):
return f"({' ,'.join(repr(x) for x in self.values)})"
class tuple_type(base_type):
def __init__(self, types, fields=None):
self.types = types
self.fields = fields or [''] * len(types)
self.name = '[' + ','.join([f"{k}:{v}" for k, v in zip(self.fields, self.types)]) + ']'
def __str__(self):
return self.name
def __iter__(self):
return iter(self.types)
def _flatten_ir_types(self, builder: ir.builder, out: List[ir.type]):
for ty in self.types:
if not isinstance(ty, constexpr):
ty._flatten_ir_types(builder, out)
def __getitem__(self, index: int) -> dtype:
return self.types[index]
def __eq__(self, other):
return type(self) is type(other) and self.types == other.types and self.fields == other.fields
def _unflatten_ir(self, handles: List[ir.value], cursor: int) -> Tuple[tuple, int]:
values = []
for ty in self.types:
value, cursor = ty._unflatten_ir(handles, cursor)
values.append(value)
return tuple(values, self), cursor
def mangle(self):
return 'T' + '_'.join(ty.mangle for ty in self.types) + 'T'
class dtype_3_4(base_type):
SINT_TYPES = ['int8', 'int16', 'int32', 'int64']
UINT_TYPES = ['int1', 'uint8', 'uint16', 'uint32', 'uint64']
FP_TYPES = ['fp8e4b15', 'fp8e4nv', 'fp8e4b8', 'fp8e5', 'fp8e5b16', 'fp16', 'bf16', 'fp32', 'fp64']
STANDARD_FP_TYPES = ['fp16', 'bf16', 'fp32', 'fp64']
OTHER_TYPES = ['void']
class SIGNEDNESS(Enum):
SIGNED = 0
UNSIGNED = 1
class KIND(Enum):
BOOLEAN = 0
INTEGRAL = 1
FLOATING = 2
def __init__(self, name):
name = _unwrap_if_constexpr(name)
self.name = name
assert name in dtype_3_4.SINT_TYPES + dtype_3_4.UINT_TYPES + dtype_3_4.FP_TYPES + dtype_3_4.OTHER_TYPES, name
self.primitive_bitwidth = get_primitive_bitwidth(name)
self.itemsize = self.primitive_bitwidth // 8
if name in dtype_3_4.SINT_TYPES:
self.int_signedness = dtype_3_4.SIGNEDNESS.SIGNED
self.int_bitwidth = self.primitive_bitwidth
elif name in dtype_3_4.UINT_TYPES:
self.int_signedness = dtype_3_4.SIGNEDNESS.UNSIGNED
self.int_bitwidth = self.primitive_bitwidth
elif name in dtype_3_4.FP_TYPES:
if name == 'fp8e4b15':
self.fp_mantissa_width = 3
self.exponent_bias = 15
elif name == 'fp8e4nv':
self.fp_mantissa_width = 3
self.exponent_bias = 7
elif name == 'fp8e4b8':
self.fp_mantissa_width = 3
self.exponent_bias = 8
elif name == 'fp8e5':
self.fp_mantissa_width = 2
self.exponent_bias = 15
elif name == 'fp8e5b16':
self.fp_mantissa_width = 2
self.exponent_bias = 16
elif name == 'fp16':
self.fp_mantissa_width = 10
self.exponent_bias = 15
elif name == 'bf16':
self.fp_mantissa_width = 7
self.exponent_bias = 127
elif name == 'fp32':
self.fp_mantissa_width = 23
self.exponent_bias = 127
elif name == 'fp64':
self.fp_mantissa_width = 52
self.exponent_bias = 1023
else:
raise RuntimeError(f'Unsupported floating-point type {name}')
def is_fp8(self):
return 'fp8' in self.name
def is_fp8e4nv(self):
return self.name == 'fp8e4nv'
def is_fp8e4b8(self):
return self.name == 'fp8e4b8'
def is_fp8e4b15(self):
return self.name == 'fp8e4b15'
def is_fp8e5(self):
return self.name == 'fp8e5'
def is_fp8e5b16(self):
return self.name == 'fp8e5b16'
def is_fp16(self):
return self.name == 'fp16'
def is_bf16(self):
return self.name == 'bf16'
def is_fp32(self):
return self.name == 'fp32'
def is_fp64(self):
return self.name == 'fp64'
def is_int1(self):
return self.name == 'int1'
def is_int8(self):
return self.name == 'int8'
def is_int16(self):
return self.name == 'int16'
def is_int32(self):
return self.name == 'int32'
def is_int64(self):
return self.name == 'int64'
def is_uint8(self):
return self.name == 'uint8'
def is_uint16(self):
return self.name == 'uint16'
def is_uint32(self):
return self.name == 'uint32'
def is_uint64(self):
return self.name == 'uint64'
def is_floating(self):
return self.name in dtype_3_4.FP_TYPES
def is_standard_floating(self):
return self.name in dtype_3_4.STANDARD_FP_TYPES
def is_int_signed(self):
return self.name in dtype_3_4.SINT_TYPES
def is_int_unsigned(self):
return self.name in dtype_3_4.UINT_TYPES
def is_int(self):
return self.name in dtype_3_4.SINT_TYPES + dtype_3_4.UINT_TYPES
def is_bool(self):
return self.is_int1()
def kind(self):
if self.is_bool():
return dtype_3_4.KIND.BOOLEAN
elif self.is_int():
return dtype_3_4.KIND.INTEGRAL
else:
assert self.is_floating()
return dtype_3_4.KIND.FLOATING
def get_int_max_value(self):
if self.is_int_signed():
return 2**(self.int_bitwidth - 1) - 1
if self.is_int_unsigned():
return 2**self.int_bitwidth - 1
assert False
def get_int_min_value(self):
if self.is_int_signed():
return -2**(self.int_bitwidth - 1)
if self.is_int_unsigned():
return 0
assert False
@staticmethod
def is_dtype(type_str):
return type_str in dtype_3_4.SINT_TYPES + dtype_3_4.UINT_TYPES + dtype_3_4.FP_TYPES + dtype_3_4.OTHER_TYPES
@staticmethod
def is_void():
raise RuntimeError("Not implemented")
@staticmethod
def is_block():
return False
@staticmethod
def is_ptr():
return False
@staticmethod
def is_const():
return False
def __eq__(self, other: dtype):
if not isinstance(other, dtype):
return False
return self.name == other.name
def __hash__(self):
return hash((self.name, ))
@property
def scalar(self):
return self
def _flatten_ir_types(self, builder: ir.builder, out: List[ir.type]) -> None:
out.append(self.to_ir(builder))
def to_ir(self, builder: ir.builder) -> ir.type:
if self.name.startswith("fp8"):
if self.name not in builder.options.supported_fp8_dtypes:
raise ValueError(f'type {self} not supported in this architecture. '
f'The supported fp8 dtypes are {builder.options.supported_fp8_dtypes}')
if self.name == 'void':
return builder.get_void_ty()
elif self.name == 'int1':
return builder.get_int1_ty()
elif self.name in ('int8', 'uint8'):
return builder.get_int8_ty()
elif self.name in ('int16', 'uint16'):
return builder.get_int16_ty()
elif self.name in ('int32', 'uint32'):
return builder.get_int32_ty()
elif self.name in ('int64', 'uint64'):
return builder.get_int64_ty()
elif self.name == 'fp8e5':
return builder.get_fp8e5_ty()
elif self.name == 'fp8e5b16':
return builder.get_fp8e5b16_ty()
elif self.name == 'fp8e4nv':
return builder.get_fp8e4nv_ty()
elif self.name == 'fp8e4b8':
return builder.get_fp8e4b8_ty()
elif self.name == 'fp8e4b15':
return builder.get_fp8e4b15_ty()
elif self.name == 'fp16':
return builder.get_half_ty()
elif self.name == 'bf16':
return builder.get_bf16_ty()
elif self.name == 'fp32':
return builder.get_float_ty()
elif self.name == 'fp64':
return builder.get_double_ty()
raise ValueError(f'fail to convert {self} to ir type')
def __str__(self):
return self.name
def codegen_name(self):
if self.name.startswith("fp"):
return "float" + self.name[2:]
elif self.name.startswith("bf"):
return "bfloat" + self.name[2:]
else:
return self.name
@property
def cache_key_part(self) -> str:
"""See cache_key_part() in triton.cc."""
return self.name
def __repr__(self):
"""Output of repr needs to be an evaluatable expression"""
return f'triton.language.{self.codegen_name()}'
def _unflatten_ir(self, handles: List[ir.value], cursor: int) -> Tuple[base_value, int]:
return tensor(handles[cursor], self), cursor + 1
def mangle(self) -> str:
if self.is_int():
SIGNED = dtype_3_4.SIGNEDNESS.SIGNED
prefix = 'i' if self.int_signedness == SIGNED else 'u'
return prefix + str(self.int_bitwidth)
if self.is_floating():
return str(self)
if self.is_void():
return 'V'
return super().mangle()
def with_element_ty(self, element_ty: dtype):
assert not self.is_block()
return element_ty
class block_type_3_4(dtype_3_4):
def __init__(self, element_ty: dtype_3_4, shape: List):
self.element_ty = element_ty
assert (isinstance(shape, (list, tuple)))
self.shape = tuple(_unwrap_shape_3_4(shape))
if not self.shape:
raise TypeError('0d block_type is forbidden')
self.numel = validate_block_shape(self.shape)
self.name = f'<{self.shape}, {self.element_ty}>'
def to_ir(self, builder: ir.builder) -> ir.block_type:
return builder.get_block_ty(self.element_ty.to_ir(builder), self.shape)
def __str__(self):
return self.name
def __repr__(self):
return self.__str__()
def is_block(self):
return True
def get_block_shapes(self) -> Tuple[int]:
return self.shape
def with_element_ty(self, scalar_ty: dtype_3_4) -> block_type_3_4:
return block_type_3_4(scalar_ty, self.shape)
def __eq__(self, other) -> bool:
if not isinstance(other, block_type_3_4):
return False
return self.element_ty == other.element_ty and self.shape == other.shape
@property
def scalar(self):
return self.element_ty
def mangle(self) -> str:
elt = self.scalar.mangle()
shape = '_'.join(map(str, self.shape))
return f'{elt}S{shape}S'
class tensor_descriptor_base_type(base_type):
def __init__(self, block_type: block_type):
self.block_type = block_type
def _unflatten_ir(self, handles: List[ir.value], cursor: int) -> Tuple[tensor_descriptor_base, int]:
value = tensor_descriptor_base(handles[cursor], self.block_type)
return value, cursor + 1
def _flatten_ir_types(self, builder: ir.builder, out: List[ir.type]) -> None:
is_signed = self.block_type.element_ty.is_int_signed()
out.append(builder.create_tensor_descriptor_type(self.block_type.to_ir(builder), is_signed))
def __str__(self) -> str:
return f"tensor_descriptor<{self.block_type}>"
def __eq__(self, other) -> bool:
if type(other) is not type(self):
return False
return self.block_type == other.block_type
def __neq__(self, other) -> bool:
return not (self == other)
def mangle(self) -> str:
return f"TD{self.block_type.mangle()}"
class tensor_descriptor_base(base_value):
""""
A tensor descriptor with unknown shape and strides
"""
def __init__(self, handle, block_type: block_type_3_4):
"""Not called by user code."""
super().__init__(handle)
self.handle = handle
self.type = tensor_descriptor_base_type(block_type)
def _flatten_ir(self, handles: List[ir.value]) -> None:
handles.append(self.handle)
@property
def block_type(self):
return self.type.block_type
@property
def block_shape(self):
return self.type.block_type.shape
@property
def dtype(self):
return self.type.block_type.element_ty
def __str__(self) -> str:
return str(self.type)
@builtin
def load(self, offsets: Sequence[constexpr | tensor], _builder=None) -> tensor:
"""Load a block from the descriptor starting at the given element offsets.
Values outside of the tensor bounds will be filled with zeros.
:note: Offset must be a multiple of 16-bytes
"""
return semantic.descriptor_load(self, offsets, "", "", _builder)
@builtin
def store(self, offsets: Sequence[constexpr | tensor], value: tensor, _builder=None) -> tensor:
"""Store a block from the descriptor starting at the given element offsets.
Values outside of the tensor bounds will be ignored.
:note: Offset must be a multiple of 16-bytes
"""
return semantic.descriptor_store(self, value, offsets, _builder)
class tensor_descriptor_type(tensor_descriptor_base_type):
def __init__(self, block_type: block_type_3_4, shape_type: tuple_type, strides_type: tuple_type):
self.block_type = block_type
self.shape_type = shape_type
self.strides_type = strides_type
def _unflatten_ir(self, handles: List[ir.value], cursor: int) -> Tuple[tensor_descriptor_base, int]:
handle = handles[cursor]
cursor += 1
shape, cursor = self.shape_type._unflatten_ir(handles, cursor)
strides, cursor = self.strides_type._unflatten_ir(handles, cursor)
shape = shape.values
strides = strides.values
value = tensor_descriptor(handle, shape, strides, self.block_type)
return value, cursor
def _flatten_ir_types(self, builder: ir.builder, out: List[ir.type]) -> None:
super()._flatten_ir_types(builder, out)
self.shape_type._flatten_ir_types(builder, out)
self.strides_type._flatten_ir_types(builder, out)
def __eq__(self, other):
return super().__eq__(other) and (self.shape_type == other.shape_type) and (self.strides_type
== other.strides_type)
class tensor_descriptor(tensor_descriptor_base):
"""A descriptor representing a tensor in global memory.
"""
def __init__(self, handle, shape: List[tensor], strides: List[tensor], block_type: block_type_3_4):
"""Not called by user code."""
super().__init__(handle, block_type)
self.shape = tuple(shape)
self.strides = tuple(strides)
self.type = tensor_descriptor_type(
block_type,
shape_type=self.shape.type,
strides_type=self.strides.type,
)
def _flatten_ir(self, handles: List[ir.value]) -> None:
handles.append(self.handle)
self.shape._flatten_ir(handles)
self.strides._flatten_ir(handles)
@builtin
def load_tensor_descriptor(desc: tensor_descriptor_base, offsets: Sequence[Union[constexpr, tensor]],
_builder=None) -> tensor:
"""Load a block of data from a tensor descriptor."""
return desc.load(offsets, _builder=_builder)
@builtin
def store_tensor_descriptor(desc: tensor_descriptor_base, offsets: Sequence[Union[constexpr, tensor]], value: tensor,
_builder=None) -> tensor:
"""Store a block of data to a tensor descriptor."""
return desc.store(offsets, value, _builder=_builder)
@builtin
def make_tensor_descriptor(
base: tensor,
shape: List[tensor],
strides: List[tensor],
block_shape: List[constexpr],
_builder=None,
) -> tensor_descriptor:
"""Make a tensor descriptor object
:param base: the base pointer of the tensor, must be 16-byte aligned
:param shape: A list of non-negative integers representing the tensor shape
:param strides: A list of tensor strides. Leading dimensions must be multiples
of 16-byte strides and the last dimension must be contiguous.
:param block_shape: The shape of block to be loaded/stored from global memory
Notes
*****
On NVIDIA GPUs with TMA support, this will result in a TMA descriptor object
and loads and stores from the descriptor will be backed by the TMA hardware.
Currently only 2-5 dimensional tensors are supported.
Example
*******
.. code-block:: python
@triton.jit
def inplace_abs(in_out_ptr, M, N, M_BLOCK: tl.constexpr, N_BLOCK: tl.constexpr):
desc = tl.make_tensor_descriptor(
in_out_ptr,
shape=[M, N],
strides=[N, 1],
block_shape=[M_BLOCK, N_BLOCK],
)
moffset = tl.program_id(0) * M_BLOCK
noffset = tl.program_id(1) * N_BLOCK
value = desc.load([moffset, noffset])
desc.store([moffset, noffset], tl.abs(value))
# TMA descriptors require a global memory allocation
def alloc_fn(size: int, alignment: int, stream: Optional[int]):
return torch.empty(size, device="cuda", dtype=torch.int8)
triton.set_allocator(alloc_fn)
M, N = 256, 256
x = torch.randn(M, N, device="cuda")
M_BLOCK, N_BLOCK = 32, 32
grid = (M // M_BLOCK, N // N_BLOCK)
inplace_abs[grid](x, M, N, M_BLOCK, N_BLOCK)
"""
return semantic.make_tensor_descriptor(base, shape, strides, block_shape, _builder)
