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android / platform / external / pytorch / 5b6ba4110be80934bc18461bc8f04d1cb2b6b148 / . / torch / _inductor / lowering.py
blob: 3caab47287fef746bb7ae2fe0a6059fd52a131fd [file] [log] [blame]
import functools
import itertools
import logging
import os
import warnings
from collections import defaultdict
from collections.abc import Iterable
from typing import Any, List, Optional, Tuple, Union
import sympy
import torch
import torch.fx
import torch.utils._pytree as pytree
from torch._prims_common import (
canonicalize_dim,
canonicalize_dims,
check,
dtype_to_type,
elementwise_dtypes,
ELEMENTWISE_TYPE_PROMOTION_KIND,
is_boolean_dtype,
is_float_dtype,
is_integer_dtype,
Number,
type_to_dtype,
)
from torch.fx.experimental.symbolic_shapes import magic_methods, method_to_operator
from torch.utils._pytree import tree_flatten
from torch.utils._sympy.functions import CeilDiv, FloorDiv, ModularIndexing
from .._dynamo.utils import import_submodule
from . import config, inductor_prims, ir, test_operators # NOQA: F401
from .decomposition import decompositions, get_decompositions
from .ir import (
ExpandView,
IndexingConstant,
is_triton,
ops_wrapper,
PermuteView,
Pointwise,
Reduction,
SqueezeView,
TensorBox,
validate_ir,
View,
)
from .utils import ceildiv, decode_device, is_dynamic, pad_listlike, sympy_product
from .virtualized import ops, V
log = logging.getLogger(__name__)
lowerings = {}
layout_constraints = {}
fallbacks = set()
aten = torch.ops.aten
tr_c10d = torch.ops.tr_c10d
prims = torch.ops.prims
needs_realized_inputs = set()
foreach_ops = set()
def assert_nyi(cond, msg):
if not cond:
raise NotImplementedError(f"inductor does not support {msg}")
def add_needs_realized_inputs(fn):
if isinstance(fn, (list, tuple, set)):
return [add_needs_realized_inputs(x) for x in fn]
needs_realized_inputs.add(fn)
if isinstance(fn, torch._ops.OpOverloadPacket):
for overload in fn.overloads():
needs_realized_inputs.add(getattr(fn, overload))
def add_layout_constraint(fn, constraint):
if isinstance(fn, torch._ops.OpOverloadPacket):
for overload in fn.overloads():
layout_constraints[getattr(fn, overload)] = constraint
else:
layout_constraints[fn] = constraint
add_needs_realized_inputs(
[
aten.as_strided,
aten.avg_pool2d,
aten.avg_pool2d_backward,
aten.bmm,
aten.convolution,
aten.convolution_backward,
aten.max_pool2d_with_indices,
aten.max_pool2d_with_indices_backward,
aten.mm,
aten.upsample_nearest2d,
aten.upsample_bicubic2d,
aten._int_mm,
]
)
# TODO(jansel): ezyang says we won't need this in the future, try removing it
# based on https://github.com/pytorch/pytorch/blob/9e3eb329df8f701/c10/core/ScalarType.h#L28
DTYPE_ID_LOOKUP = {
0: torch.uint8,
1: torch.int8,
2: torch.int16,
3: torch.int32,
4: torch.int64,
5: torch.float16,
6: torch.float32,
7: torch.float64,
8: torch.complex32,
9: torch.complex64,
10: torch.complex32,
11: torch.bool,
15: torch.bfloat16,
# TODO(jansel): add quantized types?
# _(c10::qint8, QInt8) /* 12 */
# _(c10::quint8, QUInt8) /* 13 */
# _(c10::qint32, QInt32) /* 14 */
# _(c10::quint4x2, QUInt4x2) /* 16 */
# _(c10::quint2x4, QUInt2x4) /* 17 */
}
def decode_dtype(dtype: int):
if not isinstance(dtype, int):
return dtype
assert dtype in DTYPE_ID_LOOKUP, f"id {dtype} missing from DTYPE_ID_LOOKUP"
dtype = DTYPE_ID_LOOKUP[dtype]
return dtype
def is_integer_type(x):
if isinstance(x, TensorBox):
return is_integer_dtype(x.get_dtype()) or is_boolean_dtype(x.get_dtype())
elif isinstance(x, sympy.Symbol):
return x.is_integer is True # type: ignore[attr-defined]
else:
return isinstance(x, int)
def is_boolean_type(x):
if isinstance(x, TensorBox):
return is_boolean_dtype(x.get_dtype())
else:
return isinstance(x, bool)
def get_promoted_dtype(*args, type_promotion_kind: ELEMENTWISE_TYPE_PROMOTION_KIND):
def construct_input(inp):
if isinstance(inp, (Number, sympy.Symbol)):
return inp
else:
assert hasattr(inp, "get_dtype")
dim = len(inp.get_size())
# construct a tmp tensor to feed into torch.result_type
return torch.zeros([1] * dim, dtype=inp.get_dtype())
inps = [construct_input(arg) for arg in args]
_, dtype = elementwise_dtypes(*inps, type_promotion_kind=type_promotion_kind)
return dtype
def get_overloads(aten_fn):
if not isinstance(aten_fn, (list, tuple)):
aten_fn = [aten_fn]
else:
aten_fn = list(aten_fn)
for fn in list(aten_fn):
if isinstance(fn, torch._ops.OpOverloadPacket):
for overload in fn.overloads():
other_fn = getattr(fn, overload)
if other_fn not in lowerings:
aten_fn.append(other_fn)
return aten_fn
def transform_args(args, broadcast, type_promotion_kind, convert_input_to_bool):
indices = [i for i, x in enumerate(args) if isinstance(x, TensorBox)]
if (type_promotion_kind or convert_input_to_bool) and indices:
if convert_input_to_bool:
dtype = torch.bool
else:
# FIXME that's a crude approximation for promoting args
promoting_args = [
a for a in args if isinstance(a, Number) or hasattr(a, "get_dtype")
]
dtype = get_promoted_dtype(
*promoting_args, type_promotion_kind=type_promotion_kind
)
# sometimes args are an immutable list so we can't mutate them
def promote(arg):
if isinstance(arg, TensorBox):
return to_dtype(arg, dtype)
elif isinstance(arg, ir.Constant):
return ir.Constant(arg.value, dtype, args[indices[0]].get_device())
else:
return arg
args = [promote(a) for a in args]
if broadcast and indices:
for i, x in zip(indices, broadcast_tensors(*[args[i] for i in indices])):
args[i] = x
for i in range(len(args)):
if isinstance(args[i], ir.Constant):
args[i] = ExpandView.create(args[i], list(args[indices[0]].get_size()))
return args
def _register_foreach_lowering(aten_fn, decomp_fn):
"""
Add a foreach lowering to lowerings dict.
Arguments:
aten_fn: torch.ops.aten.* fn we are lowering
decomp_fn: alternate implementation on our IR
broadcast: True to apply broadcasting to tensor inputs
type_promotion_kind: kind of type promotion applied to tensor inputs, `None` means no type promotion
convert_input_to_bool: some logical ops require inputs are converted to bool
"""
@functools.wraps(decomp_fn)
def wrapped(*args, **kwargs):
assert len(args) <= 2
out = decomp_fn(*args, **kwargs)
validate_ir(out)
return out
aten_fns = get_overloads(aten_fn)
foreach_ops.update(aten_fns)
lowerings.update({fn: wrapped for fn in aten_fns})
return wrapped
def _register_lowering(
aten_fn, decomp_fn, broadcast, type_promotion_kind, convert_input_to_bool
):
"""
Add a lowering to lowerings dict
Arguments:
aten_fn: torch.ops.aten.* fn we are lowering
decomp_fn: alternate implementation on our IR
broadcast: True to apply broadcasting to tensor inputs
type_promotion_kind: kind of type promotion applied to tensor inputs, `None` means no type promotion
convert_input_to_bool: some logical ops require inputs are converted to bool
"""
@functools.wraps(decomp_fn)
def wrapped(*args, **kwargs):
args: Union[List[Any], Tuple[Any, ...]] = list(args)
unpacked = False
# TODO maybe we need to use pytrees here
if len(args) == 1 and isinstance(args[0], (list, tuple)):
unpacked = True
args = args[0]
# explicitly assert for "out=" ops for better error messages
assert not any(
x == "out" for x in kwargs.keys()
), "out= ops aren't yet supported"
# kwargs tensors not supported yet unless it's a fallback op
assert not any(isinstance(x, TensorBox) for x in kwargs.values()) or all(
fn in fallbacks for fn in aten_fn
)
args = transform_args(
args, broadcast, type_promotion_kind, convert_input_to_bool
)
if unpacked:
args = [args]
out = decomp_fn(*args, **kwargs)
validate_ir(out)
return out
aten_fn = get_overloads(aten_fn)
lowerings.update({fn: wrapped for fn in aten_fn})
return wrapped
def register_lowering(
aten_fn,
broadcast=False,
type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
convert_input_to_bool=False,
):
"""
Shim to support decorator syntax.
"""
return functools.partial(
_register_lowering,
aten_fn,
broadcast=broadcast,
type_promotion_kind=type_promotion_kind,
convert_input_to_bool=convert_input_to_bool,
)
def broadcast_symbolic_shapes(a, b):
"""
Broadcasting logic based on symbolic shapes.
We give the shapes 0 and 1 concrete values, while all other shapes
are symbolic sympy formulas.
"""
output = []
for a, b in itertools.zip_longest(
reversed(a), reversed(b), fillvalue=sympy.Integer(1)
):
if b == 1:
output.append(a)
elif a == 1:
output.append(b)
else:
V.graph.sizevars.guard_equals(a, b)
if len(sympy.expand(b).free_symbols) < len(sympy.expand(a).free_symbols):
output.append(b) # prefer shorter formula
else:
output.append(a)
return tuple(reversed(output))
def promote_constants(inputs, override_return_dtype=None):
if not any(isinstance(x, (sympy.Expr, int, float)) for x in inputs):
return inputs
if all(isinstance(x, (int, float, sympy.Symbol)) for x in inputs):
dtype = override_return_dtype or get_promoted_dtype(
*inputs, type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
)
def const_func(x):
if isinstance(x, sympy.Symbol):
return ir.IndexingConstant(x, dtype, decode_device(None))
else:
return ir.Constant(x, dtype, decode_device(None))
return [const_func(x) for x in inputs]
ex = next(x for x in inputs if isinstance(x, (TensorBox, ExpandView)))
out = []
for x in inputs:
if isinstance(x, (int, float)):
out.append(
ExpandView.create(
ir.Constant(x, ex.get_dtype(), ex.get_device()), list(ex.get_size())
)
)
elif isinstance(x, sympy.Expr):
out.append(IndexingConstant(x, ex.get_dtype(), ex.get_device()))
else:
out.append(x)
return out
def make_pointwise(
fn,
override_return_dtype=None,
override_device=None,
override_fn_when_input_bool=None,
override_fn_when_cuda_float64=None,
allow_alpha=False,
):
def inner(*inputs: List[TensorBox], alpha=None):
inputs = promote_constants(inputs, override_return_dtype)
if allow_alpha:
if alpha is not None and alpha != 1:
inputs = list(inputs)
inputs[-1] = mul(inputs[-1], alpha)
else:
assert alpha is None
loaders = [x.make_loader() for x in inputs]
ranges = inputs[0].get_size()
dtype = override_return_dtype or inputs[0].get_dtype()
is_cuda = decode_device(inputs[0].get_device()).type == "cuda"
for other in inputs[1:]:
assert isinstance(other, ir.BaseConstant) or len(ranges) == len(
other.get_size()
), f"ndim mismatch {fn} {ranges} {other.get_size()}"
def inner_fn(index):
assert len(index) == len(ranges), f"wrong ndim {index} {ranges}"
if dtype == torch.bool and override_fn_when_input_bool is not None:
return override_fn_when_input_bool(*[load(index) for load in loaders])
elif override_fn_when_cuda_float64 and is_cuda and dtype == torch.float64:
return override_fn_when_cuda_float64(*[load(index) for load in loaders])
else:
return fn(*[load(index) for load in loaders])
if not override_device:
device = None
for i in inputs:
if i.get_device().type == "cuda":
device = i.get_device()
break
if not device:
device = inputs[0].get_device()
device = override_device or device
return Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=ranges,
)
return inner
def make_foreach_pointwise(pw_fn, allow_alpha=False):
def inner(*inputs: List[List[TensorBox]], alpha=1):
# group by device, whether any of the inputs are dynamic, and whether their types match
# (proxy for type promotion)
def group_args(arg_pairs):
out = defaultdict(list)
for i, args in enumerate(arg_pairs):
use_foreach = not is_dynamic(*args)
device = None
for t in args:
if isinstance(t, TensorBox):
device = t.data.get_device() # type: ignore[attr-defined]
break
assert (
device is not None
), "foreach op should have at least one tensor arg"
out[(device, use_foreach)].append((i, args))
return out
realize_outputs = False
for node in V.graph.current_node.users:
for user in node.users:
if not (user.op == "call_function" and user.target in foreach_ops):
realize_outputs = True
a_list_input = None
for input in inputs:
if isinstance(input, (list, tuple)):
a_list_input = input
break
assert (
a_list_input is not None
), "at least one input must be a list to a foreach op"
# broadcast scalar inputs to match length of list inputs
broadcast_inputs = []
for input in inputs:
if not isinstance(input, (list, tuple)):
broadcast_inputs.append([input] * len(a_list_input))
else:
broadcast_inputs.append(input)
groups = group_args(zip(*broadcast_inputs))
outputs = [None] * len(a_list_input)
for (device, use_foreach), group in groups.items():
buffer_list = []
for (
output_ind,
args,
) in group:
if allow_alpha:
output = pw_fn(*args, alpha=alpha)
else:
output = pw_fn(*args)
outputs[output_ind] = output
if device.type == "cuda" and use_foreach and realize_outputs:
buffer_list.append(output.realize())
if buffer_list:
V.graph.register_list(buffer_list)
assert all(x is not None for x in outputs)
return outputs
return inner
def to_dtype(x: TensorBox, dtype: torch.dtype, copy=False):
if x.get_dtype() == dtype:
return clone(x) if copy else x
def _to_dtype(x):
return ops.to_dtype(x, dtype)
return make_pointwise(_to_dtype, override_return_dtype=dtype)(x)
@register_lowering(prims.convert_element_type, type_promotion_kind=None)
def _convert_element_type(x: TensorBox, dtype: torch.dtype):
return to_dtype(x, dtype, copy=True)
def to_dtype_bitcast(x: TensorBox, dtype: torch.dtype, *, copy=False):
if x.get_dtype() == dtype:
return clone(x) if copy else x
def _get_primitive_bitwidth(dtype):
if dtype.is_floating_point:
return torch.finfo(dtype).bits
else:
return torch.iinfo(dtype).bits
src_bits = _get_primitive_bitwidth(x.get_dtype())
dst_bits = _get_primitive_bitwidth(dtype)
if src_bits != dst_bits:
raise NotImplementedError(
f"bitcast {x.get_dtype()} to different bitwidth type {dtype} is not supported yet."
)
def _to_dtype_bitcast(x):
return ops.to_dtype_bitcast(x, dtype)
return make_pointwise(_to_dtype_bitcast, override_return_dtype=dtype)(x)
@register_lowering(aten.view.dtype, type_promotion_kind=None)
def _view_dtype(x: TensorBox, dtype: torch.dtype):
return to_dtype_bitcast(x, dtype, copy=True)
def to_device(x: TensorBox, device: torch.device, *, copy=False):
device = decode_device(device)
if x.get_device() == device:
return clone(x) if copy else x
return TensorBox.create(ir.DeviceCopy.create(x, device))
@register_lowering(prims.device_put, type_promotion_kind=None)
def _device_put(x: TensorBox, device: torch.device):
return to_device(x, device, copy=True)
def register_pointwise(
aten_fn,
name=None,
broadcast=True,
type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
convert_input_to_bool=False,
override_return_dtype=None,
override_fn_when_input_bool=None,
allow_alpha=False,
use_libdevice_for_f64=False,
):
"""A pointwise function that maps ops.{name} to inputs"""
name = name or aten_fn.__name__
fn = ops_wrapper(name)
if use_libdevice_for_f64:
fn_libdevice = ops_wrapper("libdevice_" + name)
if override_fn_when_input_bool is not None:
override_fn_when_input_bool = ops_wrapper(override_fn_when_input_bool)
fn = make_pointwise(
fn,
override_return_dtype=override_return_dtype,
override_fn_when_input_bool=override_fn_when_input_bool,
override_fn_when_cuda_float64=fn_libdevice if use_libdevice_for_f64 else None,
allow_alpha=allow_alpha,
)
fn = register_lowering(
aten_fn,
broadcast=broadcast,
type_promotion_kind=type_promotion_kind,
convert_input_to_bool=convert_input_to_bool,
)(fn)
if hasattr(prims, name):
register_lowering(
getattr(prims, name),
type_promotion_kind=None,
convert_input_to_bool=convert_input_to_bool,
)(fn)
return fn
def register_foreach_pointwise(
aten_fn,
pointwise_lowering_fn,
allow_alpha=False,
):
fn = make_foreach_pointwise(pointwise_lowering_fn, allow_alpha=allow_alpha)
fn = _register_foreach_lowering(aten_fn, fn)
return fn
@register_lowering(aten.where, broadcast=False, type_promotion_kind=None)
def where(cond, a, b):
def fn(*args):
return ops.where(*args)
if isinstance(a, (float, int)):
a = constant_like(a)(b)
if isinstance(b, (float, int)):
b = constant_like(b)(a)
args = [cond, a, b]
dtype = get_promoted_dtype(
args[1], args[2], type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
)
indices = [i for i, x in enumerate(args) if isinstance(x, TensorBox)]
for i, x in zip(indices, broadcast_tensors(*[args[i] for i in indices])):
args[i] = x
for i in range(len(args)):
if isinstance(args[i], ir.Constant):
args[i] = ExpandView.create(args[i], list(args[indices[0]].get_size()))
return make_pointwise(fn, override_return_dtype=dtype)(
args[0], to_dtype(args[1], dtype), to_dtype(args[2], dtype)
)
@register_lowering(aten.broadcast_tensors, broadcast=False, type_promotion_kind=None)
def broadcast_tensors(*inputs):
if len(inputs) == 1 and isinstance(inputs[0], (list, tuple)):
return broadcast_tensors(*inputs[0])
target: List[sympy.Expr] = functools.reduce(
broadcast_symbolic_shapes, [x.get_size() for x in inputs], []
)
outputs = []
for x in inputs:
sizes = x.get_size()
if len(sizes) != len(target) or any(
((a == 1 and b != 1) or (a != 1 and b == 1)) for a, b in zip(sizes, target)
):
x = expand(x, target)
outputs.append(x)
return outputs
@register_lowering([aten.alias, aten.detach, aten.detach_, aten.lift, prims.view_of])
def nop(x):
return x # AOT autograd handles this for us
if hasattr(aten, "lift_fresh"):
register_lowering(aten.lift_fresh)(nop)
@register_lowering(aten.squeeze, type_promotion_kind=None)
def squeeze(x, dim=None):
assert isinstance(x, TensorBox)
if dim is None:
return TensorBox(SqueezeView.create(x.data))
dim = canonicalize_dims(len(x.get_size()), dim)
dims = set((dim,) if not isinstance(dim, tuple) else dim)
new_shape = []
for d, s in enumerate(x.get_size()):
if not (d in dims and V.graph.sizevars.evaluate_expr(sympy.Eq(s, 1))):
new_shape.append(s)
# squeeze does nothing if the size isn't 1
return view(x, new_shape) if new_shape != x.get_size() else x
@register_lowering(aten.squeeze_copy, type_promotion_kind=None)
def squeeze_copy(x, dim=None):
return clone(squeeze(x, dim))
@register_lowering([aten.squeeze_])
def squeeze_(x, dim=None):
val = squeeze(x, dim)
assert isinstance(x, TensorBox)
assert isinstance(val, TensorBox)
x.data = val.data
return x
@register_lowering(aten.isinf)
def isinf(x):
if is_integer_type(x):
return full_like(x, False, dtype=torch.bool)
fn = ops_wrapper("isinf")
return make_pointwise(fn, override_return_dtype=torch.bool)(x)
@register_lowering(aten.isnan)
def isnan(x):
if is_integer_type(x):
return full_like(x, False, dtype=torch.bool)
fn = ops_wrapper("isnan")
return make_pointwise(fn, override_return_dtype=torch.bool)(x)
@register_lowering(aten.ceil)
def ceil(x):
if is_integer_type(x):
return clone(x)
fn = ops_wrapper("ceil")
return make_pointwise(fn)(x)
@register_lowering(aten.floor)
def floor(x):
if is_integer_type(x):
return clone(x)
fn = ops_wrapper("floor")
return make_pointwise(fn)(x)
@register_lowering(aten.round)
def round(x):
if is_integer_type(x):
return clone(x)
fn = ops_wrapper("round")
return make_pointwise(fn)(x)
@register_lowering(aten.trunc)
def trunc(x):
if is_integer_type(x):
return clone(x)
fn = ops_wrapper("trunc")
return make_pointwise(fn)(x)
@register_lowering(aten.expand, type_promotion_kind=None)
def expand(x, sizes):
(x,) = promote_constants([x])
if isinstance(x, ir.BaseConstant):
return ExpandView.create(x, tuple(sizes))
assert isinstance(x, TensorBox)
assert isinstance(sizes, (list, tuple))
if tuple(x.get_size()) == tuple(sizes):
return x
x_size_product = V.graph.sizevars.size_hint(sympy_product(x.get_size()))
if x_size_product > 0:
# maybe realize input before broadcasting it
x.mark_reuse(V.graph.sizevars.size_hint(sympy_product(sizes)) // x_size_product)
return TensorBox(ExpandView.create(x.data, tuple(sizes)))
@register_lowering(prims.broadcast_in_dim, type_promotion_kind=None)
def broadcast_in_dim(a, shape, broadcast_dimensions):
s = list(shape)
for broadcast_dimension in broadcast_dimensions:
s[broadcast_dimension] = -1
v = a
for idx, x in enumerate(s):
if x != -1:
v = unsqueeze(v, idx)
return expand(v, shape)
@register_lowering(aten.expand_as, type_promotion_kind=None)
def expand_as(x, y):
return expand(x, y.get_size())
@register_lowering(aten.repeat)
def repeat(x, repeats):
old_size = list(x.get_size())
if len(repeats) > len(old_size):
old_size = [sympy.Integer(1)] * (len(repeats) - len(old_size)) + old_size
x = view(x, list(old_size))
assert len(repeats) == len(x.get_size())
new_size = list(x.get_size())
zero_tensor = False
for i in range(len(repeats)):
if repeats[i] == 0:
zero_tensor = True
new_size[i] = new_size[i] * repeats[i]
if zero_tensor:
return empty(new_size, dtype=x.get_dtype(), device=x.get_device())
if all((a == 1 or b == 1) for a, b in zip(repeats, old_size)):
return expand(x, new_size)
def inner_fn(index):
assert len(index) == len(repeats)
index = list(index)
for i in range(len(repeats)):
if repeats[i] != 1:
if old_size[i] == 1:
index[i] = sympy.Integer(0)
else:
index[i] = ModularIndexing(index[i], 1, old_size[i])
return x_loader(index)
old_size_product = V.graph.sizevars.size_hint(sympy_product(old_size))
if old_size_product > 0:
# maybe realize the input
x.mark_reuse(
V.graph.sizevars.size_hint(sympy_product(new_size)) // old_size_product
)
x_loader = x.make_loader()
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=inner_fn,
ranges=list(new_size),
)
@register_lowering(aten._unsafe_view, type_promotion_kind=None)
@register_lowering(aten.view, type_promotion_kind=None)
@register_lowering(aten.reshape, type_promotion_kind=None)
def view(x, sizes):
assert isinstance(x, TensorBox)
assert isinstance(sizes, (list, tuple))
return TensorBox(View.create(x.data, sizes))
@register_lowering(aten.permute, type_promotion_kind=None)
def permute(x, dims):
assert isinstance(x, TensorBox)
assert isinstance(dims, (list, tuple))
return TensorBox(PermuteView.create(x.data, tuple(dims)))
@register_lowering(aten.slice, type_promotion_kind=None)
def slice_(x, dim=0, start=0, end=2**63, step=1):
assert isinstance(x, TensorBox)
dim = _validate_dim(x, dim, 0)
dim_size = x.get_size()[dim]
if V.graph.sizevars.evaluate_expr(sympy.Lt(start + dim_size, 0)):
start = 0
if V.graph.sizevars.evaluate_expr(sympy.Lt(end + dim_size, 0)):
end = 0
return TensorBox(ir.SliceView.create(x.data, dim, start, end, step))
@register_lowering(aten.roll, type_promotion_kind=None)
def roll(a, shifts, dims=tuple()):
"""
This is based on torch._refs.roll(), but uses ModularIndexing().
We can't use the ref here because it is based on multiple calls to
torch.cat() that this will result in terrible code.
"""
# ATen specifies int[1] type for shifts and dims which expands integers to tuples of length 1
if not isinstance(shifts, Iterable):
shifts = (shifts,)
if not isinstance(dims, Iterable):
dims = (dims,)
dims = [_validate_dim(a, d) for d in dims]
if sympy_product(a.get_size()) == 0:
return clone(a)
len_shifts = len(shifts)
len_dims = len(dims)
if len_shifts != 1 or len_dims != 1:
if len_shifts == 0:
raise RuntimeError("`shifts` required")
# Takes care of the case when dims is not specified (default)
# By default, the tensor is flattened before shifting, after which the original shape is restored
if len_dims == 0 and len_shifts == 1:
flat = view(a, [sympy_product(a.get_size())])
rolled = roll(flat, shifts, 0)
return view(rolled, list(a.get_size()))
if len_shifts != len_dims:
raise RuntimeError(
f"shifts and dimensions must align. shifts: {len_shifts}, dims: {len_dims}"
)
tail_shifts = shifts[1:]
tail_dims = dims[1:]
first_dim_rolled = roll(a, shifts[0], dims[0])
return roll(first_dim_rolled, tail_shifts, tail_dims)
(dim,) = dims
# TODO: Avoid guarding on shape here
size = V.graph.sizevars.evaluate_static_shape(a.get_size()[dim])
start = (size - shifts[0]) % size
a_loader = a.make_loader()
def fn(index):
index = list(index)
index[dim] = ModularIndexing(
index[dim] + start, sympy.Integer(1), sympy.expand(size)
)
return a_loader(index)
return Pointwise.create(
device=a.get_device(),
dtype=a.get_dtype(),
inner_fn=fn,
ranges=a.get_size(),
)
@register_lowering(aten.as_strided, type_promotion_kind=None)
def as_strided(x, size, stride, storage_offset=None):
if isinstance(x, TensorBox) and isinstance(x.data, ir.BaseView):
# as_strided ignores views
x = x.data.unwrap_view()
x.realize()
if not ir.is_storage_and_layout(x):
raise NotImplementedError(f"unrealized as_strided({x}, ...)")
storage, old_layout = ir.as_storage_and_layout(x)
new_layout = ir.FixedLayout(
old_layout.device,
old_layout.dtype,
[sympy.expand(s) for s in size],
[sympy.expand(s) for s in stride],
sympy.expand(storage_offset or 0),
)
return TensorBox(ir.ReinterpretView(storage, new_layout))
@register_lowering(aten.as_strided_, type_promotion_kind=None)
def as_strided_(x, size, stride, storage_offset=None):
assert isinstance(x, TensorBox)
x.data = as_strided(x, size, stride, storage_offset).data
return x
@register_lowering(aten.as_strided_copy, type_promotion_kind=None)
def as_strided_copy(x, size, stride, storage_offset=None):
result = as_strided(x, size, stride, storage_offset)
return clone(result)
@register_lowering(aten.cat)
def cat(inputs, dim=0):
if all(input.get_dtype() is torch.uint8 for input in inputs):
# TODO <leslie> Remove this fallback when we support vectorization
# code gen with uint8 data type directly.
for input in inputs:
input.realize()
if all(len(input.layout.size) == 4 for input in inputs):
inputs, _ = require_channels_last(aten.cat, *inputs)
return fallback_handler(aten.cat)(inputs, dim)
if len(inputs) == 1:
return clone(inputs[0])
dim = _validate_dim(inputs[0], dim, 0)
dtype = get_promoted_dtype(
*inputs, type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT
)
inputs = [to_dtype(inp, dtype) for inp in inputs]
return TensorBox(ir.ConcatKernel.create(inputs, dim))
@register_lowering(aten.diagonal, type_promotion_kind=None)
def diagonal(input, offset: int = 0, dim1: int = 0, dim2: int = 1):
original_shape = input.get_size()
num_dims = len(original_shape)
dim1 = canonicalize_dim(idx=dim1, rank=num_dims)
dim2 = canonicalize_dim(idx=dim2, rank=num_dims)
check(
dim1 != dim2, lambda: f"diagonal dimensions cannot be identical {dim1}, {dim2}"
)
offset_negative = V.graph.sizevars.evaluate_expr(sympy.Lt(offset, 0))
if offset_negative:
diag_size = max(min(original_shape[dim1] + offset, original_shape[dim2]), 0)
else:
diag_size = max(min(original_shape[dim1], original_shape[dim2] - offset), 0)
base_idx = (0, 0)
if offset_negative:
base_idx = (-offset, 0)
else:
base_idx = (0, offset)
sizes = [s for i, s in enumerate(original_shape) if i not in (dim1, dim2)]
sizes.append(diag_size)
def reindexer(idx):
diag_idx = idx[-1]
original_idx = [0] * len(original_shape)
cur_dim = 0
for d in range(num_dims):
if d == dim1:
original_idx[d] = diag_idx + base_idx[0]
elif d == dim2:
original_idx[d] = diag_idx + base_idx[1]
else:
original_idx[d] = idx[cur_dim]
cur_dim += 1
assert cur_dim == len(original_shape) - 2
return original_idx
return TensorBox(ir.GenericView.create(input, sizes, reindexer))
@register_lowering(aten.diagonal_copy, type_promotion_kind=None)
def diagonal_copy(input, offset: int = 0, dim1: int = 0, dim2: int = 1):
return clone(diagonal(input, offset, dim1, dim2))
@register_lowering(aten.diagonal_scatter, type_promotion_kind=None)
def diagonal_scatter(input, src, offset: int = 0, dim1: int = 0, dim2: int = 1):
output = clone(input)
target = diagonal(output, offset, dim1, dim2)
mutate_to(target, src)
return output
@register_lowering(aten.select, type_promotion_kind=None)
def select(x, dim, idx):
idx = View.handle_negative_index(idx, x.get_size()[dim])
return squeeze(slice_(x, dim, idx, idx + 1), dim)
@register_lowering(aten.split, type_promotion_kind=None)
def split(x, sizes, dim=0):
dim = _validate_dim(x, dim, 0)
x_size = V.graph.sizevars.evaluate_static_shape(x.get_size()[dim])
if isinstance(sizes, sympy.Expr):
# TODO: We don't have to guard on sizes per se, but the number
# of splits must stay constant
sizes = V.graph.sizevars.evaluate_static_shape(sizes)
if isinstance(sizes, (int, sympy.Integer)):
sizes = [sizes] * ((x_size + sizes - 1) // sizes)
result = []
start = 0
for size in sizes:
end = start + size
result.append(slice_(x, dim, start, end))
start = end
return result
@register_lowering(aten.split_with_sizes, type_promotion_kind=None)
def split_with_sizes(x, sizes, dim=0):
return split(x, sizes, dim)
@register_lowering(aten.unbind, type_promotion_kind=None)
def unbind(x, dim=0):
dim = _validate_dim(x, dim, 0)
x_size = V.graph.sizevars.evaluate_static_shape(x.get_size()[dim])
result = []
for i in range(x_size):
result.append(select(x, dim, i))
return result
@register_lowering(aten.unfold, type_promotion_kind=None)
def unfold(x, dimension, size, step):
sizes = x.get_size()
ndim = len(sizes)
dim = canonicalize_dim(ndim, dimension)
if ndim == 0:
return slice_(unsqueeze(x, 0), end=size)
sizevars = V.graph.sizevars
sizevars.guard_leq(size, sizes[dim])
sizevars.guard_lt(0, step)
new_dim_size = FloorDiv(sizes[dim] - size, step) + 1
x.mark_reuse(sizevars.size_hint(CeilDiv(new_dim_size * size, sizes[dim])))
out_size = [*sizes[:dim], new_dim_size, *sizes[dim + 1 :], size]
def reindexer(idx):
dim_idx = idx[-1] + idx[dim] * step
return (*idx[:dim], dim_idx, *idx[dim + 1 : -1])
return TensorBox(ir.GenericView.create(x, out_size, reindexer))
@register_lowering(aten.unsqueeze, type_promotion_kind=None)
def unsqueeze(x, dim):
dim = _validate_dim(x, dim, 1)
new_shape = list(x.get_size())
new_shape.insert(dim, sympy.Integer(1))
return view(x, new_shape)
@register_lowering(aten.unsqueeze_, type_promotion_kind=None)
def unsqueeze_(x, dim):
val = unsqueeze(x, dim)
assert isinstance(x, TensorBox)
assert isinstance(val, TensorBox)
x.data = val.data
return x
def _validate_dim(x, dim, offset=0):
assert isinstance(dim, int)
ndim = len(x.get_size())
if dim < 0:
dim += ndim + offset
assert 0 <= dim < ndim + offset
return dim
@register_lowering(aten.glu)
def glu(x, dim=-1):
dim = _validate_dim(x, dim, 0)
# TODO: don't guard on static shape here
new_len = V.graph.sizevars.evaluate_static_shape(x.get_size()[dim]) // 2
a = slice_(x, dim, 0, new_len)
b = slice_(x, dim, new_len, new_len * 2)
return mul(a, sigmoid(b))
def register_onednn_fusion_ops():
if torch._C._has_mkldnn:
cpu_needs_realized_inputs = [
torch.ops.mkldnn._convolution_pointwise,
torch.ops.mkldnn._convolution_pointwise_,
torch.ops.mkldnn._convolution_transpose_pointwise,
torch.ops.mkldnn._linear_pointwise,
aten.mkldnn_rnn_layer.default,
torch.ops.onednn.qconv2d_pointwise,
]
@register_lowering(torch.ops.mkldnn._convolution_pointwise)
def convolution_unary(
x: TensorBox,
weight: TensorBox,
bias: TensorBox,
padding,
stride,
dilation,
groups,
attr,
scalars,
algorithm,
):
return TensorBox.create(
ir.ConvolutionUnary.create(
x,
weight,
bias,
padding,
stride,
dilation,
groups,
attr,
scalars,
algorithm,
)
)
@register_lowering(torch.ops.mkldnn._convolution_pointwise.binary)
def convolution_binary(
x: TensorBox,
other: TensorBox,
weight: TensorBox,
bias: TensorBox,
padding,
stride,
dilation,
groups,
binary_attr,
binary_alpha,
unary_attr,
unary_scalars,
unary_algorithm,
):
return TensorBox.create(
ir.ConvolutionBinary.create(
x,
other,
weight,
bias,
padding,
stride,
dilation,
groups,
binary_attr,
binary_alpha,
unary_attr,
unary_scalars,
unary_algorithm,
)
)
@register_lowering(torch.ops.mkldnn._convolution_pointwise_.binary)
def convolution_binary_inplace(
x: TensorBox,
other: TensorBox,
weight: TensorBox,
bias: TensorBox,
padding,
stride,
dilation,
groups,
binary_attr,
binary_alpha,
unary_attr,
unary_scalars,
unary_algorithm,
):
return TensorBox.create(
ir.ConvolutionBinaryInplace.create(
x,
other,
weight,
bias,
padding,
stride,
dilation,
groups,
binary_attr,
binary_alpha,
unary_attr,
unary_scalars,
unary_algorithm,
)
)
@register_lowering(torch.ops.mkldnn._linear_pointwise)
def linear_unary(
x: TensorBox, w: TensorBox, b: TensorBox, attr, scalars, algorithm
):
return TensorBox.create(
ir.LinearUnary.create(x, w, b, attr, scalars, algorithm)
)
@register_lowering(torch.ops.mkldnn._linear_pointwise.binary)
def linear_binary(x: TensorBox, y: TensorBox, w: TensorBox, b: TensorBox, attr):
return TensorBox.create(ir.LinearBinary.create(x, y, w, b, attr))
@register_lowering(torch.ops.mkldnn._convolution_transpose_pointwise)
def convolution_transpose_unary(
x: TensorBox,
weight: TensorBox,
bias: TensorBox,
padding,
output_padding,
stride,
dilation,
groups,
attr,
scalars,
algorithm,
):
return TensorBox.create(
ir.ConvolutionTransposeUnary.create(
x,
weight,
bias,
padding,
output_padding,
stride,
dilation,
groups,
attr,
scalars,
algorithm,
)
)
@register_lowering(aten.mkldnn_rnn_layer.default)
def mkldnn_rnn_layer(
x: TensorBox,
w0: TensorBox,
w1: TensorBox,
w2: TensorBox,
w3: TensorBox,
hx: TensorBox,
cx: TensorBox,
reverse: bool,
batch_sizes: List[int],
mode: int,
hidden_size: int,
num_layers: int,
has_biases: bool,
bidirectional: bool,
batch_first: bool,
train: bool,
):
return pytree.tree_map(
TensorBox.create,
ir.MkldnnRnnLayer.create(
x,
w0,
w1,
w2,
w3,
hx,
cx,
reverse,
batch_sizes,
mode,
hidden_size,
num_layers,
has_biases,
bidirectional,
batch_first,
train,
),
)
@register_lowering(torch.ops.onednn.qconv2d_pointwise, type_promotion_kind=None)
def qconvolution_unary(
x: TensorBox,
x_scale,
x_zp,
packed_weight: TensorBox,
w_scale: TensorBox,
w_zp: TensorBox,
bias: TensorBox,
stride,
padding,
dilation,
groups,
o_inv_scale,
o_zero_point,
fp32_output,
attr,
scalars,
algorithm,
):
return TensorBox.create(
ir.QConvPointWisePT2E.create(
x,
x_scale,
x_zp,
packed_weight,
w_scale,
w_zp,
bias,
stride,
padding,
dilation,
groups,
o_inv_scale,
o_zero_point,
fp32_output,
attr,
scalars,
algorithm,
)
)
@register_lowering(
torch.ops.onednn.qconv2d_pointwise.binary, type_promotion_kind=None
)
def qconvolution_binary(
x: TensorBox,
x_scale,
x_zp,
accum: TensorBox,
accum_scale,
accum_zp,
packed_weight: TensorBox,
w_scale: TensorBox,
w_zp: TensorBox,
bias: TensorBox,
stride,
padding,
dilation,
groups,
o_inv_scale,
o_zero_point,
fp32_output,
binary_attr,
alpha,
unary_attr,
unary_scalars,
unary_algorithmm,
):
return TensorBox.create(
ir.QConvPointWiseBinaryPT2E.create(
x,
x_scale,
x_zp,
accum,
accum_scale,
accum_zp,
packed_weight,
w_scale,
w_zp,
bias,
stride,
padding,
dilation,
groups,
o_inv_scale,
o_zero_point,
fp32_output,
binary_attr,
alpha,
unary_attr,
unary_scalars,
unary_algorithmm,
)
)
@register_lowering(torch.ops.onednn.qlinear_pointwise, type_promotion_kind=None)
def qlinear_unary(
x: TensorBox,
x_scale,
x_zp,
packed_weight: TensorBox,
w_scale: TensorBox,
w_zp: TensorBox,
bias: TensorBox,
o_inv_scale,
o_zero_point,
fp32_output,
attr,
scalars,
algorithm,
):
return TensorBox.create(
ir.QLinearPointwisePT2E.create(
x,
x_scale,
x_zp,
packed_weight,
w_scale,
w_zp,
bias,
o_inv_scale,
o_zero_point,
fp32_output,
attr,
scalars,
algorithm,
)
)
if torch._C.has_mkl:
cpu_needs_realized_inputs.append(torch.ops.mkl._mkl_linear)
@register_lowering(torch.ops.mkl._mkl_linear)
def mkl_packed_linear(
x: TensorBox,
packed_w: TensorBox,
orig_w: TensorBox,
b: TensorBox,
batch_size,
):
result = TensorBox.create(
ir.MKLPackedLinear.create(x, packed_w, orig_w, batch_size)
)
if b is not None:
result = add(result, b)
return result
add_needs_realized_inputs(cpu_needs_realized_inputs)
else:
pass
register_onednn_fusion_ops()
def fallback_handler(kernel, add_to_fallback_set=True):
if add_to_fallback_set:
fallbacks.add(kernel)
def handler(*args, **kwargs):
return pytree.tree_map(
TensorBox.create, ir.FallbackKernel.create(kernel, *args, **kwargs)
)
return handler
@functools.lru_cache(None)
def _warn_complex_not_supported():
warnings.warn(
"Torchinductor does not support code generation for complex operators. Performance may be worse than eager."
)
@functools.lru_cache(None)
def _warn_float8_not_supported():
warnings.warn(
"Torchinductor does not support code generation for float8 operators. Performance may be worse than eager."
)
# There are some types (CPU) which we accept as input but not as
# output.
def unsupported_input_tensor(t: torch._subclasses.FakeTensor):
"Do not support reading or writing to this tensor"
if t.is_complex():
_warn_complex_not_supported()
return True
# FP8 Tensors are currently not supported
if t.dtype in {torch.float8_e4m3fn, torch.float8_e5m2}:
_warn_float8_not_supported()
return True
return False
def unsupported_output_tensor(t: torch._subclasses.FakeTensor):
"Do not support writing tensor but can read from it"
if unsupported_input_tensor(t):
return True
return t.is_cpu and config.disable_cpp_codegen
def fallback_node_due_to_unsupported_type(node: torch.fx.Node, allow_cpu_inputs=True):
# Custom fallback lowering
if node.target is aten.view_as_complex.default:
return False
# We should be able to remove this special case once `disable_cpp_codegen` is killed.
if node.target is aten.lift_fresh_copy.default:
return False
def check_skip_condition(node, is_output):
if not isinstance(node, torch.fx.Node):
return False
if "val" not in node.meta:
return False
for meta in tree_flatten(node.meta["val"])[0]:
if not isinstance(meta, torch._subclasses.FakeTensor):
continue
if is_output:
if unsupported_output_tensor(meta):
return True
else:
if unsupported_input_tensor(meta):
return True
return False
# only skip codegen if there is a cpu output, not input
for arg in tree_flatten((node.args, node.kwargs))[0]:
if check_skip_condition(arg, is_output=False):
return True
return check_skip_condition(node, is_output=True)
def make_fallback(kernel, layout_constraint=None, warn=True):
assert (
kernel not in decompositions
), f"both a fallback and a decomp for same kernel: {kernel}"
if get_decompositions([kernel]) and warn and bool(os.getenv("CI")):
# Note: 'warn' is holdover from when this was a warning, but for ops that previously
# set warn=False we do not want a CI error.
# Ignore the 'suppress errors' configs in CI, as this particular warning happens on startup anyway and is not
# likely to be triggered preferentially on one CI config over another.
if torch._dynamo.config.suppress_errors:
torch._dynamo.config.suppress_errors = False
log.warning(
"A make_fallback error occured in suppress_errors config,"
" and suppress_errors is being disabled to surface it."
)
raise AssertionError(
f"make_fallback({kernel}): a decomposition exists, we should switch to it."
" To fix this error, either add a decomposition to core_aten_decompositions (preferred)"
" or inductor_decompositions, and delete the corresponding `make_fallback` line."
" Get help from the inductor team if unsure, don't pick arbitrarily to unblock yourself.",
)
add_needs_realized_inputs(kernel)
if layout_constraint is not None:
add_layout_constraint(kernel, layout_constraint)
return register_lowering(kernel, type_promotion_kind=None)(fallback_handler(kernel))
def philox_rand_offset(shape):
"""
TorchInductor offset calculation differs from PyTorch eager offset
calculation for random ops (tl.rand vs torch.rand). In future, we should
strive for same impl for tl.rand and torch.rand.
"""
numel = 1
for s in shape:
numel = numel * s
return tensor(numel, dtype=torch.int64)
@register_lowering(torch.ops.rngprims.philox_rand, type_promotion_kind=None)
def philox_rand(size, seed, offset, stride, device, dtype):
# stride arg is optional and will be used in future for distributed random
# ops. Currently, its ununsed.
random_pos = ir.FixedLayout(
device,
dtype,
size,
ir.FlexibleLayout.contiguous_strides(size),
).make_indexer()
seed_loader = seed.make_loader()
offset_loader = offset.make_loader()
def inner_fn(index):
# Both seed and offset in the philox_rand op are tensors.
# torch seed and offsets are of type int64, but tl.rand accepts int32
seed_index_expr = ops.to_dtype(seed_loader([]), torch.int32)
offset_index_expr = ops.to_dtype(offset_loader([]), torch.int32)
# Get the offset'd position
rand_index_expr = ops.add(
ops.index_expr(random_pos(index), torch.int32), offset_index_expr
)
result = ops.rand(
seed_index_expr,
rand_index_expr,
)
return ops.to_dtype(result, dtype)
random_values_node = Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=list(size),
)
offset_node = philox_rand_offset(size)
return random_values_node, offset_node
@register_lowering(aten.native_dropout, type_promotion_kind=None)
def native_dropout(x, p, train):
if config.fallback_random:
return pytree.tree_map(
TensorBox.create, ir.FallbackKernel.create(aten.native_dropout, x, p, train)
)
else:
raise AssertionError("should be handled in replace_random.py")
@register_lowering(aten.bernoulli_, type_promotion_kind=None)
def bernoulli_(x, *args):
assert config.fallback_random or x.get_device() == torch.device(
"cpu"
), "this should be handled in decomps unless config.fallback_random or the device is CPU"
x.realize()
ir.InplaceBernoulliFallback(x, *args)
return x
@register_lowering(aten.bernoulli.p, type_promotion_kind=None)
def bernoulli_p(x, *args):
assert config.fallback_random or x.get_device() == torch.device(
"cpu"
), "this should be handled in decomps unless config.fallback_random or the device is CPU"
return bernoulli_(clone(x), *args)
# This shouldn't be called in general
@register_lowering(aten._foobar)
def _foobar(_):
raise AssertionError()
@functools.lru_cache(1)
def _warn_triton_random(salt):
log.info("using triton random, expect difference from eager")
def warn_triton_random():
# only warn once per graph
_warn_triton_random(V.graph.creation_time)
fallback_rand = fallback_handler(aten.rand)
fallback_randn = fallback_handler(aten.randn)
make_fallback(aten.randint)
@register_lowering(aten.rand)
def rand(*args, **kwargs):
if config.fallback_random or kwargs.get("generator", None) is not None:
return fallback_rand(*args, **kwargs)
raise AssertionError("should have been handled in replace_random.py")
@register_lowering(aten.randn)
def randn(*args, **kwargs):
if config.fallback_random or kwargs.get("generator", None) is not None:
return fallback_randn(*args, **kwargs)
raise AssertionError("should have been handled in replace_random.py")
@register_lowering(inductor_prims.force_stride_order, type_promotion_kind=None)
def inductor_force_stride_order(input_tensor, stride):
stride_order = ir.get_stride_order(stride)
return ir.ExternKernel.require_stride_order(input_tensor, stride_order)
@register_lowering(inductor_prims.seed, type_promotion_kind=None)
def inductor_seed(device: torch.device):
raise AssertionError("should be handled in fuse_seed_creation_pass()")
@register_lowering(inductor_prims.seeds, type_promotion_kind=None)
def inductor_seeds(count, device):
warn_triton_random()
return TensorBox.create(ir.RandomSeeds(count, decode_device(device)))
@register_lowering(inductor_prims.lookup_seed, type_promotion_kind=None)
def inductor_lookup_seed(seeds, index):
def inner_fn(_):
return ops.load_seed(seeds.get_name(), index)
return Pointwise.create(
device=seeds.get_device(),
dtype=seeds.get_dtype(),
inner_fn=inner_fn,
ranges=[],
)
@register_lowering(inductor_prims.random, type_promotion_kind=None)
def inductor_random(size: List[int], seed: TensorBox, mode: str, *, offset: int = 0):
assert not config.fallback_random
assert mode in ("rand", "randn")
size = [*size]
dtype = torch.float32
device = seed.get_device()
random_pos = ir.FixedLayout(
device, dtype, size, ir.FlexibleLayout.contiguous_strides(size), offset=offset
).make_indexer()
seed_loader = seed.make_loader()
def inner_fn(index):
return getattr(ops, mode)(
seed_loader([]),
ops.index_expr(random_pos(index), torch.int32),
)
result = Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=[*size],
)
result.realize()
return result
@register_lowering(inductor_prims.randint, type_promotion_kind=None)
def inductor_randint(
low: int, high: int, size: List[int], seed: TensorBox, *, offset: int = 0
):
assert not config.fallback_random
size = [*size]
dtype = torch.int64
device = seed.get_device()
random_pos = ir.FixedLayout(
device, dtype, size, ir.FlexibleLayout.contiguous_strides(size), offset=offset
).make_indexer()
seed_loader = seed.make_loader()
def inner_fn(index):
return ops.randint64(
seed_loader([]),
ops.index_expr(random_pos(index), torch.int32),
low,
high,
)
return Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=[*size],
)
@register_lowering(aten.bucketize, type_promotion_kind=None)
def bucketize(
input: TensorBox,
boundaries: TensorBox,
*,
out_int32: bool = False,
right: bool = False,
):
assert len(boundaries.get_size()) == 1
if not (is_triton(input) and is_triton(boundaries)):
return fallback_handler(aten.bucketize, add_to_fallback_set=False)(
input, boundaries, out_int32=out_int32, right=right
)
# The entire boundaries tensor needs to be used by ops.bucketize, so we
# need to realize it into global memory; or in other words, we can't
# guarantee that boundaries.get_name() (used below) will exist unless
# we call boundaries.realize().
boundaries.realize()
boundaries_size = boundaries.get_size()[0]
boundaries_loader = boundaries.make_loader()
device = input.get_device()
input_loader = input.make_loader()
index_dtype = torch.int32 if out_int32 else torch.int64
def inner_fn(index):
val = input_loader(index)
indices = ops.bucketize(
val,
boundaries.get_name(),
boundaries_size,
index_dtype,
right,
)
return indices
return Pointwise.create(
device=device,
dtype=index_dtype,
inner_fn=inner_fn,
ranges=input.get_size(),
)
def require_dense(_, *args, **kwargs):
args, kwargs = pytree.tree_map_only(
ir.IRNode, lambda t: ir.ExternKernel.require_stride1(t), (args, kwargs)
)
return args, kwargs
def require_contiguous(_, *args, **kwargs):
args, kwargs = pytree.tree_map_only(
ir.IRNode, lambda t: ir.ExternKernel.require_contiguous(t), (args, kwargs)
)
return args, kwargs
def require_channels_last(_, *args, **kwargs):
args, kwargs = pytree.tree_map_only(
ir.IRNode, lambda t: ir.ExternKernel.require_channels_last(t), (args, kwargs)
)
return args, kwargs
def constrain_to_fx_strides(fx_node, *args, **kwargs):
def apply_constraint(arg, fx_arg):
if isinstance(arg, ir.IRNode):
stride_order = ir.get_stride_order(fx_arg.meta["val"].stride())
return ir.ExternKernel.require_stride_order(arg, stride_order)
return arg
args = tuple(
apply_constraint(arg, fx_arg) for arg, fx_arg in zip(args, fx_node.args)
)
kwargs = {k: apply_constraint(v, fx_node.kwargs[k]) for k, v in kwargs.items()}
return args, kwargs
# TODO(jansel): we should implement decomps or lowerings for these
# https://github.com/pytorch/torchdynamo/issues/327
FALLBACK_ALLOW_LIST = {
"torchvision::roi_align",
}
make_fallback(aten._adaptive_avg_pool2d_backward, require_dense)
make_fallback(aten.convolution_backward, constrain_to_fx_strides)
make_fallback(aten._cudnn_rnn, require_dense)
make_fallback(aten._cudnn_rnn_backward, require_contiguous)
make_fallback(aten.cumsum, require_dense, warn=False)
make_fallback(aten.cumprod, require_dense, warn=False)
make_fallback(aten._embedding_bag, require_contiguous)
make_fallback(aten._embedding_bag_forward_only, require_contiguous)
make_fallback(aten._flash_attention_forward)
make_fallback(aten._flash_attention_backward)
make_fallback(aten._fused_moving_avg_obs_fq_helper)
make_fallback(aten._fused_moving_avg_obs_fq_helper_functional)
make_fallback(aten.grid_sampler_2d_backward, require_dense)
make_fallback(aten.randperm)
make_fallback(aten._scaled_dot_product_efficient_attention)
make_fallback(aten._scaled_dot_product_efficient_attention_backward)
make_fallback(aten._scaled_dot_product_flash_attention, warn=False)
make_fallback(aten._scaled_dot_product_flash_attention_backward)
make_fallback(aten.sort)
make_fallback(aten.sort.stable)
make_fallback(aten._sparse_coo_tensor_with_dims_and_tensors)
make_fallback(aten._thnn_fused_lstm_cell, require_dense)
make_fallback(aten.topk)
make_fallback(aten.upsample_bicubic2d_backward, require_contiguous)
make_fallback(aten._scaled_mm.default)
make_fallback(aten.view_as_complex, require_contiguous)
# The following were added as a result of https://github.com/pytorch/pytorch/pull/94039 to pass tests
# It's not necessarily a priority to implement these
make_fallback(aten.upsample_linear1d)
make_fallback(aten.upsample_trilinear3d)
make_fallback(aten.upsample_linear1d_backward)
make_fallback(aten.upsample_trilinear3d_backward)
make_fallback(aten._adaptive_avg_pool3d)
make_fallback(aten.adaptive_max_pool2d)
make_fallback(aten.adaptive_max_pool3d)
make_fallback(aten.addbmm)
make_fallback(aten.addmv, warn=False)
make_fallback(aten._addmm_activation, warn=False)
make_fallback(aten.avg_pool3d)
make_fallback(aten.block_diag)
make_fallback(aten._cdist_forward)
make_fallback(aten.cummax)
make_fallback(aten.cummin)
make_fallback(aten.cumprod, warn=False)
make_fallback(aten.digamma, warn=False)
make_fallback(aten._efficientzerotensor)
make_fallback(aten._embedding_bag_per_sample_weights_backward)
make_fallback(aten._efficientzerotensor)
make_fallback(aten._embedding_bag_per_sample_weights_backward)
make_fallback(aten.fractional_max_pool2d)
make_fallback(aten.fractional_max_pool3d)
make_fallback(aten.frexp)
make_fallback(aten.geqrf)
make_fallback(aten.histc)
make_fallback(aten.i0)
make_fallback(aten.igamma, warn=False)
make_fallback(aten.igammac, warn=False)
make_fallback(aten.isin)
make_fallback(aten.kthvalue)
make_fallback(aten.linalg_cholesky_ex)
make_fallback(aten.linalg_cross)
make_fallback(aten._linalg_det)
make_fallback(aten.linalg_householder_product)
make_fallback(aten.linalg_inv_ex)
make_fallback(aten.linalg_ldl_factor_ex)
make_fallback(aten.linalg_ldl_solve)
make_fallback(aten.linalg_lu)
make_fallback(aten.linalg_lu_factor_ex)
make_fallback(aten.linalg_lu_solve)
make_fallback(aten.linalg_matrix_exp)
make_fallback(aten.linalg_qr)
make_fallback(aten._linalg_slogdet)
make_fallback(aten._linalg_solve_ex)
make_fallback(aten.linalg_solve_triangular)
make_fallback(aten._linalg_svd)
make_fallback(aten.logcumsumexp)
make_fallback(aten.lu_unpack)
make_fallback(aten.max_pool3d_with_indices)
make_fallback(aten.max_unpool2d)
make_fallback(aten.max_unpool3d)
make_fallback(aten.median)
make_fallback(aten.mode)
make_fallback(aten.nanmedian)
make_fallback(aten.ormqr)
make_fallback(aten._pdist_forward)
make_fallback(aten.pixel_shuffle)
make_fallback(aten.pixel_unshuffle)
make_fallback(aten.polygamma)
make_fallback(aten.put)
make_fallback(aten.reflection_pad1d)
make_fallback(aten.replication_pad1d)
make_fallback(aten.resize)
make_fallback(aten.resize_)
make_fallback(aten.resize_as)
make_fallback(aten.resize_as_)
make_fallback(aten.searchsorted)
make_fallback(aten.special_airy_ai)
make_fallback(aten.special_bessel_j0, warn=False)
make_fallback(aten.special_bessel_j1, warn=False)
make_fallback(aten.special_bessel_y0, warn=False)
make_fallback(aten.special_bessel_y1)
make_fallback(aten.special_chebyshev_polynomial_t)
make_fallback(aten.special_chebyshev_polynomial_u)
make_fallback(aten.special_erfcx, warn=False)
make_fallback(aten.special_hermite_polynomial_h)
make_fallback(aten.special_hermite_polynomial_he)
make_fallback(aten.special_i0e, warn=False)
make_fallback(aten.special_i1, warn=False)
make_fallback(aten.special_i1e, warn=False)
make_fallback(aten.special_laguerre_polynomial_l)
make_fallback(aten.special_modified_bessel_i0)
make_fallback(aten.special_modified_bessel_i1)
make_fallback(aten.special_modified_bessel_k0)
make_fallback(aten.special_modified_bessel_k1)
make_fallback(aten.special_ndtri, warn=False)
make_fallback(aten.special_scaled_modified_bessel_k0)
make_fallback(aten.special_scaled_modified_bessel_k1)
make_fallback(aten.special_spherical_bessel_j0, warn=False)
make_fallback(aten.special_zeta, warn=False)
make_fallback(aten.take)
make_fallback(aten._trilinear)
make_fallback(aten.uniform, warn=False)
make_fallback(aten.unsafe_split, warn=False)
make_fallback(aten._adaptive_avg_pool3d_backward)
make_fallback(aten.adaptive_max_pool2d_backward)
make_fallback(aten.adaptive_max_pool3d_backward)
make_fallback(aten.avg_pool3d_backward)
make_fallback(aten._cdist_backward)
make_fallback(aten._embedding_bag_dense_backward)
make_fallback(aten.fractional_max_pool2d_backward)
make_fallback(aten.fractional_max_pool3d_backward)
make_fallback(aten._linalg_check_errors)
make_fallback(aten.max_pool3d_with_indices_backward)
make_fallback(aten._pdist_backward)
make_fallback(aten.reflection_pad1d_backward)
make_fallback(aten.replication_pad1d_backward)
make_fallback(aten.soft_margin_loss_backward, warn=False)
make_fallback(aten.linalg_pinv.atol_rtol_tensor)
make_fallback(aten.segment_reduce.default)
make_fallback(aten._segment_reduce_backward.default)
make_fallback(aten.angle)
make_fallback(aten.cholesky_inverse)
make_fallback(aten.cholesky_solve)
make_fallback(aten._fft_r2c)
make_fallback(aten.histogram.bin_ct)
make_fallback(aten._histogramdd_bin_edges.default)
make_fallback(aten._histogramdd_from_bin_cts.default)
make_fallback(aten.index_reduce)
make_fallback(aten.masked_scatter)
make_fallback(aten.to_sparse)
make_fallback(aten._to_sparse)
make_fallback(aten.triangular_solve)
make_fallback(aten.gcd.default, warn=False)
make_fallback(aten._linalg_eigh)
make_fallback(aten.zeros.names)
make_fallback(torch._prims.rng_prims.run_and_save_rng_state)
make_fallback(torch._prims.rng_prims.run_with_rng_state)
# fails accuracy on test_torch.py, and explicit fallback required to avoid warn=True on implicit
make_fallback(aten.exponential.default, warn=False)
# Register with type_promotion_kind None.
# For example, fp16.copy_(fp32) should **not** promote the first input's dtype.
@register_lowering(aten.copy, type_promotion_kind=None)
def copy(self, src, non_blocking=False):
x = src
if self.get_device() != src.get_device():
x = to_device(x, self.get_device())
if self.get_dtype() != src.get_dtype():
x = to_dtype(x, self.get_dtype())
if self.get_size() != src.get_size():
out = expand(x, self.get_size())
return clone(out)
return clone(x)
@register_lowering(aten.clone)
def clone(x, *, memory_format=None):
# TODO(jansel): memory format
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=x.make_loader(),
ranges=list(x.get_size()),
)
if hasattr(aten, "lift_fresh_copy"):
register_lowering(aten.lift_fresh_copy)(clone)
@register_lowering(prims.iota)
def iota(
length,
*,
start,
step,
dtype,
device,
requires_grad,
):
def fn(index):
return ops.index_expr(step * index[0] + start, dtype=dtype)
return Pointwise.create(
device=decode_device(device),
dtype=dtype,
inner_fn=fn,
ranges=[length],
)
@register_lowering(aten.select_scatter, type_promotion_kind=None)
def select_scatter(x, src, dim: int, index: int):
assert x.get_dtype() == src.get_dtype()
x_loader = x.make_loader()
dim = _validate_dim(x, dim, 0)
if V.graph.sizevars.evaluate_expr(sympy.Lt(index, 0)):
index = index + x.get_size()[dim]
V.graph.sizevars.guard_leq(0, index)
V.graph.sizevars.guard_lt(index, x.get_size()[dim])
src = expand(unsqueeze(src, dim), x.get_size())
src_loader = src.make_loader()
def inner_fn(idx):
return ops.where(
ops.eq(
ops.index_expr(idx[dim], torch.int32),
ops.index_expr(index, torch.int32),
),
src_loader(idx),
x_loader(idx),
)
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=inner_fn,
ranges=list(x.get_size()),
)
@register_lowering(aten.slice_scatter, type_promotion_kind=None)
def slice_scatter(x, src, dim=0, start=None, end=None, step=1):
assert x.get_dtype() == src.get_dtype()
x_loader = x.make_loader()
dim = _validate_dim(x, dim, 0)
dim_size = x.get_size()[dim]
if start is not None and V.graph.sizevars.evaluate_expr(sympy.Lt(start, 0)):
start = start + dim_size
if end is not None and V.graph.sizevars.evaluate_expr(sympy.Lt(end, 0)):
end = end + dim_size
if start is None:
start = 0
if end is None or V.graph.sizevars.statically_known_leq(x.get_size()[dim], end):
end = dim_size
src_size = list(x.get_size())
src_size[dim] = FloorDiv(sympy.expand(end - start), sympy.expand(step))
src = expand(src, src_size)
src_loader = src.make_loader()
def inner_fn(idx):
if start == 0 and end == dim_size and step == 1:
# selecting every element is the same as just src.clone()
return src_loader(idx)
idx_dim = ops.index_expr(idx[dim], torch.int64)
src_idx = list(idx)
src_idx[dim] = FloorDiv(idx[dim] - start, step)
mask = []
if start != 0:
mask.append(
ops.ge(
idx_dim,
ops.index_expr(sympy.expand(start), torch.int64),
)
)
if end != dim_size:
mask.append(
ops.lt(
idx_dim,
ops.index_expr(sympy.expand(end), torch.int64),
)
)
if step != 1:
mask.append(
ops.eq(
ops.index_expr(
ModularIndexing(idx[dim] - start, 1, step), torch.int64
),
ops.constant(0, torch.torch.int64),
)
)
assert mask
mask = functools.reduce(ops.and_, mask)
src_val = ops.masked(
mask,
lambda: src_loader(src_idx),
0 if is_integer_type(x) else 0.0,
)
return ops.where(
mask,
src_val,
x_loader(idx),
)
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=inner_fn,
ranges=list(x.get_size()),
)
def _unwrap(x):
if isinstance(x, (list, tuple)) and len(x) > 0:
return _unwrap(x[0])
return x
@register_lowering([torch.tensor, aten.scalar_tensor])
def tensor(data, *, dtype=None, device=None, layout=None, pin_memory=False):
assert_nyi(layout in (None, torch.strided), f"layout={layout}")
assert_nyi(not pin_memory, "pin_memory")
if isinstance(_unwrap(data), int):
dtype = dtype or torch.int64
else:
dtype = dtype or torch.get_default_dtype()
ranges: List[sympy.Expr] = []
if isinstance(data, sympy.Expr):
def inner_fn(index):
return ops.index_expr(data, dtype)
elif isinstance(data, (float, int)):
def inner_fn(index):
return ops.constant(data, dtype)
elif len(data) == 0 or isinstance(data[0], (float, int)) and len(data) <= 8:
# inline small tensors
ranges.append(sympy.Integer(len(data)))
def inner_fn(index):
def binary_search(start, end):
assert start < end
if end - start == 1:
return ops.constant(data[start], dtype)
mid = (end - start) // 2 + start
return ops.where(
ops.lt(
ops.index_expr(index[0], torch.int64),
ops.constant(mid, torch.int64),
),
binary_search(start, mid),
binary_search(mid, end),
)
if len(data) == 0:
return ops.constant(0, dtype)
return binary_search(0, len(data))
else:
return V.graph.add_tensor_constant(
torch.tensor(data, dtype=dtype, device=device)
)
return Pointwise.create(
device=decode_device(device),
dtype=dtype,
inner_fn=inner_fn,
ranges=ranges,
)
@register_lowering(torch.as_tensor)
def as_tensor(data, dtype=None, device=None):
if isinstance(data, TensorBox):
if dtype is not None:
data = to_dtype(data, dtype)
if device is not None:
data = to_device(data, device)
return data
return tensor(data, dtype=dtype, device=device)
@register_lowering(torch.LongTensor)
def long_tensor(data):
return tensor(data, dtype=torch.int64)
@register_lowering(aten._local_scalar_dense)
def _local_scalar_dense(data):
return ir.DynamicScalar()
def _full(fill_value, device, dtype, size):
value = fill_value
if not isinstance(fill_value, (int, float)) and hasattr(value, "value"):
value = value.value
if isinstance(value, (int, float)):
def inner_fn(index):
return ops.constant(value, dtype)
elif isinstance(value, sympy.Expr):
def inner_fn(index):
return ops.index_expr(value, dtype)
else:
assert len(value.get_size()) == 0
value_loader = value.make_loader()
def inner_fn(index):
return value_loader([])
return Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=list(size),
)
@register_lowering(aten.full_like, type_promotion_kind=None)
def full_like(x, fill_value, **kwargs):
return create_tensor_like(tensor_constructor(fill_value))(x, **kwargs)
def tensor_constructor(fill_value):
# torch.zeros, torch.ones, etc
def inner(
*size,
names=None,
dtype=None,
device=None,
layout=None,
pin_memory=False,
memory_format=None,
):
assert_nyi(names is None, "named tensors")
assert_nyi(layout in (None, torch.strided), f"layout={layout}")
assert_nyi(not pin_memory, "pin_memory")
device = decode_device(device)
dtype = dtype or torch.get_default_dtype()
if len(size) == 1 and isinstance(size[0], (list, tuple, torch.Size)):
size = tuple(size[0])
size = [sympy.expand(s) for s in size]
return _full(fill_value, device, dtype, size)
return inner
@register_lowering([torch.empty, aten.empty])
def empty(
*size,
names=None,
dtype=None,
layout=None,
device=None,
pin_memory=None,
memory_format=None,
):
assert_nyi(names is None, "named tensors")
device = decode_device(device)
if len(size) == 1 and isinstance(size[0], (list, tuple, torch.Size)):
size = tuple(size[0])
return empty_strided(
size, None, dtype=dtype, layout=layout, device=device, pin_memory=pin_memory
)
def create_tensor_like(creation_fn):
"""
Shim to convert X_like(...) into X(...). For example zeros_like() into zeros().
"""
def _constant_like(
x, *, dtype=None, device=None, layout=None, pin_memory=False, memory_format=None
):
assert_nyi(not pin_memory, "pin_memory")
assert_nyi(layout in (None, torch.strided), f"layout={layout}")
if dtype is None:
dtype = x.get_dtype()
else:
dtype = decode_dtype(dtype)
device = device or x.get_device()
size = list(x.get_size())
return creation_fn(
size, dtype=dtype, device=device, layout=layout, pin_memory=pin_memory
)
return _constant_like
def constant_like(fill_value):
return create_tensor_like(tensor_constructor(fill_value))
empty_like = register_lowering(aten.empty_like)(create_tensor_like(empty))
ones_like = create_tensor_like(tensor_constructor(1))
zeros_like = create_tensor_like(tensor_constructor(0))
def new_constant(fill_value):
def _new_constant(
x, size, *, dtype=None, layout=None, device=None, pin_memory=None
):
assert isinstance(size, (list, tuple))
assert_nyi(not pin_memory, "pin_memory")
assert_nyi(layout in (None, torch.strided), f"layout={layout}")
dtype = decode_dtype(dtype) or x.get_dtype()
device = device or x.get_device()
size = [sympy.Integer(s) for s in size]
return _full(fill_value, device, dtype, size)
return _new_constant
@register_lowering(aten.new_empty)
def new_empty(x, size, *, dtype=None, layout=None, device=None, pin_memory=None):
if dtype is None:
dtype = x.get_dtype()
if device is None:
device = x.get_device()
return empty_strided(
size, None, dtype=dtype, layout=layout, device=device, pin_memory=pin_memory
)
@register_lowering(aten.empty_strided)
def empty_strided(
size, stride, *, dtype=None, layout=None, device=None, pin_memory=None
):
assert isinstance(size, (list, tuple))
assert isinstance(stride, (list, tuple, type(None)))
assert_nyi(not pin_memory, "pin_memory")
assert_nyi(layout in (None, torch.strided), f"layout={layout}")
dtype = decode_dtype(dtype) or torch.get_default_dtype()
device = device or torch.tensor(0.0).device
pointwise = _full(fill_value=0, device=device, dtype=dtype, size=size)
pointwise.realize()
buffer = pointwise.data.data
# explicitly set ranges to zeros in order to make a NopKernelSchedulerNode
buffer.data.ranges = [0] * len(size)
assert isinstance(buffer, ir.ComputedBuffer)
size = [sympy.expand(s) for s in size]
stride = (
[sympy.expand(s) for s in stride]
if stride
else ir.FlexibleLayout.contiguous_strides(size)
)
buffer.layout = ir.FixedLayout(
device=device,
dtype=dtype,
size=size,
stride=stride,
)
return pointwise
@register_lowering(aten.new_empty_strided)
def new_empty_strided(
x, size, stride, *, dtype=None, layout=None, device=None, pin_memory=None
):
if dtype is None:
dtype = x.get_dtype()
if device is None:
device = x.get_device()
return empty_strided(
size, stride, dtype=dtype, layout=layout, device=device, pin_memory=pin_memory
)
@register_lowering(prims.copy_strided.default)
def copy_strided(x, stride):
stride = [V.graph.sizevars.size_hint(s) for s in stride]
stride_order = sorted(range(len(stride)), key=stride.__getitem__)
return ir.ExternKernel.require_stride_order(x, stride_order)
@register_lowering([torch.full, aten.full])
def full(size, fill_value, **kwargs):
dtype = kwargs.get("dtype")
kwargs["dtype"] = dtype if dtype is not None else type_to_dtype(type(fill_value))
return tensor_constructor(fill_value)(size, **kwargs)
@register_lowering(aten.gather, type_promotion_kind=None)
def gather(x, dim, index, sparse_grad=False):
# sparse_grad doesn't affect forward computation,
# and backward tracing is taken care of by AOT Autograd
assert isinstance(x, TensorBox)
assert index.get_dtype() == torch.int64
size = x.get_size()
offset = len(size) == 0
dim = _validate_dim(x, dim, offset)
x_loader = x.make_loader()
index_loader = index.make_loader()
def fn(idx):
idx = list(idx)
if len(idx) != 0:
idx[dim] = ops.indirect_indexing(index_loader(idx), size[dim])
return x_loader(idx)
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=fn,
ranges=index.get_size(),
)
@register_lowering(aten.embedding, type_promotion_kind=None)
def embedding(weight, indices, padding_idx=-1, scale_grad_by_freq=False, sparse=False):
assert not sparse
assert isinstance(weight, TensorBox)
assert isinstance(indices, TensorBox)
assert "int" in str(indices.get_dtype())
weight_loader = weight.make_loader()
indices_loader = indices.make_loader()
indices_ndim = len(indices.get_size())
weight_size = weight.get_size()
new_size = [*indices.get_size(), *weight_size[1:]]
def fn(idx):
assert len(idx) == len(new_size), f"{idx} != {new_size}"
var_index = indices_loader(idx[:indices_ndim])
weight_idx = [ops.indirect_indexing(var_index, weight_size[0])] + [
*idx[indices_ndim:]
]
return weight_loader(weight_idx)
return Pointwise.create(
device=weight.get_device(),
dtype=weight.get_dtype(),
inner_fn=fn,
ranges=new_size,
)
def check_and_broadcast_indices(indices, device):
assert all(
i.get_dtype() in (torch.int64, torch.int32, torch.bool, torch.uint8)
for i in indices
if i is not None
), f"indices must be int64, byte or bool. Got {[i.get_dtype() for i in indices if i is not None]}"
if any(
i.get_dtype() in (torch.bool, torch.uint8) for i in indices if i is not None
):
raise NotImplementedError("Fallback for bool indices")
valid_idxs = [i for i, x in enumerate(indices) if isinstance(x, TensorBox)]
assert len(valid_idxs) > 0, "requires at least 1 non-None index"
new_indices = [None] * len(indices)
for i, x in zip(valid_idxs, broadcast_tensors(*[indices[i] for i in valid_idxs])):
# Eager allows indices to be CPU tensor when running on CUDA
# FIXME: Calling to_device(x, device) should work but
# test_advancedindex_mixed_cpu_devices still fails
if x.get_device() != device:
raise NotImplementedError("Fallback when indices is on a different device")
new_indices[i] = x
output_dim = len(x.get_size())
start_offset = 0
# only support None at start or end for now
tmp = list(new_indices)
while tmp and tmp[-1] is None:
tmp.pop()
while tmp and tmp[0] is None:
tmp.pop(0)
start_offset += 1
if any((i is None) for i in tmp):
raise NotImplementedError("Fallback when None is in the middle of indices")
end_offset = output_dim + start_offset
return new_indices, start_offset, end_offset
def index_impl(x, indices, check):
assert isinstance(indices, (list, tuple))
x_loader = x.make_loader()
indices, start_offset, end_offset = check_and_broadcast_indices(
indices, x.get_device()
)
indices_sizes = [i.get_size() for i in indices if i is not None]
indices_loaders = [i.make_loader() for i in indices if i is not None]
# no guards on output size, all the guards are set in broadcast_tensors
output_size = list(indices_sizes[0])
x_size = x.get_size()
indexed_size = [x_size[i] for i in range(len(indices)) if indices[i] is not None]
if 0 in indexed_size and 0 not in output_size:
raise IndexError("index is out of bounds for dimension with size 0")
output_size = [
*x_size[:start_offset],
*output_size,
*x_size[start_offset + len(indices_loaders) :],
]
def fn(idx):
assert len(idx) == len(output_size)
assert len(indices_loaders) == len(indexed_size)
new_index = [
ops.indirect_indexing(
loader(idx[start_offset:end_offset]), size, check=check
)
for loader, size in zip(indices_loaders, indexed_size)
]
new_index = [*idx[:start_offset], *new_index, *idx[end_offset:]]
return x_loader(new_index)
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=fn,
ranges=output_size,
)
@register_lowering(aten.index, type_promotion_kind=None)
def index(x, indices):
try:
return index_impl(x, indices, check=True)
except NotImplementedError:
# Fallback to ATen for boolean indexing
x.realize()
return fallback_handler(aten.index)(x, indices)
@register_lowering(aten._unsafe_index, type_promotion_kind=None)
def _unsafe_index(x, indices):
return index_impl(x, indices, check=False)
# All the indexing decompositions are written in terms of index, index_put, and index_put_
# We cannot have this lowering as a decomposition as it introduces
# mutation in the graph, which is bad for Aot Autograd. Aot Autograd runs dead
# code elimination and common subexpression elimination optimizations, which
# assume graphs to be side-effect free. More details at
# https://github.com/pytorch/torchdynamo/issues/1235
# and
# https://github.com/pytorch/torchdynamo/issues/1863
@register_lowering(aten.index_put)
def index_put(x, indices, values, accumulate=False):
return index_put_(clone(x), indices, values, accumulate)
@register_lowering(aten._unsafe_index_put)
def _unsafe_index_put(x, indices, values, accumulate=False):
return index_put_impl_(clone(x), indices, values, accumulate, check=False)
def index_put_as_masked_fill(self, indices, value, accumulate):
if value.get_device() != self.get_device():
value = to_device(value, self.get_device())
if accumulate:
value = add(self, value)
return mutate_to(self, where(indices[0], value, self))
def index_put_fallback(self, indices, values, accumulate):
if is_triton(values) and (
accumulate is True or torch.are_deterministic_algorithms_enabled()
):
V.graph.disable_cudagraphs = True
ir.IndexPutFallback(self, indices, values, accumulate)
return self
@register_lowering(aten.index_put_, type_promotion_kind=None)
def index_put_(self, indices, values, accumulate=False):
return index_put_impl_(self, indices, values, accumulate, check=True)
def index_put_impl_(self, indices, values, accumulate, check):
# Dispatch to masked fill for single boolean index with single value
if (
values.get_numel() == 1
and len(indices) == 1
and indices[0].get_dtype() in {torch.bool, torch.uint8}
):
mask = indices[0]
for _ in range(len(mask.get_size()), len(self.get_size())):
mask = unsqueeze(mask, -1)
return index_put_as_masked_fill(self, [mask], values, accumulate)
# Fallback in torch deterministic mode
if torch.are_deterministic_algorithms_enabled():
return index_put_fallback(self, indices, values, accumulate)
# Fallback if there is a boolean index
for index in indices:
if index is not None and index.get_dtype() in {torch.bool, torch.uint8}:
return index_put_fallback(self, indices, values, accumulate)
x_size = self.get_size()
x_ndim = len(x_size)
# fallback to aten.index_put_, as tl.atomic_add does NOT support int64 or bool
if self.get_dtype() in {torch.int64, torch.bool}:
# self is an scalar Tensor
if x_ndim == 0:
self = view(self, [1])
self = index_put_fallback(self, indices, values, accumulate)
if x_ndim == 0:
self = view(self, [])
return self
values = to_dtype(values, self.get_dtype())
try:
indices, start_offset, end_offset = check_and_broadcast_indices(
indices, self.get_device()
)
except NotImplementedError:
return index_put_fallback(self, indices, values, accumulate)
indices_sizes = [i.get_size() for i in indices if i is not None]
indices_loaders = [i.make_loader() for i in indices if i is not None]
assert isinstance(self, TensorBox)
self.realize()
# self is an scalar Tensor
if x_ndim == 0:
self = view(self, [1])
output_size = list(indices_sizes[0])
expected_vals_size = [
*x_size[:start_offset],
*output_size,
*x_size[start_offset + len(indices_sizes) :],
]
indexed_size = [x_size[i] for i in range(len(indices)) if indices[i] is not None]
values = expand(values, expected_vals_size)
# all guards are set above during broadcast_tensors and expand
def output_indexer(index):
assert len(index) == len(expected_vals_size)
new_index = [
ops.indirect_indexing(
loader(index[start_offset:end_offset]), size, check=check
)
for loader, size in zip(indices_loaders, indexed_size)
]
new_index = [*index[:start_offset], *new_index, *index[end_offset:]]
return new_index
scatter = ir.Scatter(
device=self.get_device(),
dtype=self.get_dtype(),
inner_fn=values.make_loader(),
ranges=expected_vals_size, # iter_ranges,
output_indexer=output_indexer,
scatter_mode="atomic_add" if accumulate else None,
)
buffer = ir.ComputedBuffer(
None,
ir.MutationLayout(self),
scatter,
)
buffer.name = V.graph.register_buffer(buffer)
if x_ndim == 0:
self = view(self, [])
return self
@register_lowering(aten.as_strided_scatter, type_promotion_kind=None)
def as_strided_scatter(self, src, size, stride, storage_offset=None):
output = clone(self)
output_view = as_strided(output, size, stride, storage_offset)
copy_(output_view, src)
return output
@register_lowering(aten.scatter, type_promotion_kind=None)
def scatter(x, dim: int, index, src, **kwargs):
return scatter_(clone(x), dim, index, src, **kwargs)
def scatter_fallback(
fn,
self,
dim: int,
index,
src,
*,
reduce: Optional[str] = None,
include_self: bool = True,
):
reduce_ty = "add" if fn == "aten.scatter_" else "sum"
if (
reduce not in {None, reduce_ty}
or (
fn == "aten.scatter_reduce_"
and reduce == "sum"
and isinstance(src, TensorBox)
and src.get_device() == torch.device("cpu")
and config.cpp.fallback_scatter_reduce_sum
)
or (reduce == reduce_ty and self.get_dtype() in {torch.bool, torch.int64})
or torch.are_deterministic_algorithms_enabled()
):
ir.ScatterFallback(
fn, self, dim, index, src, reduce=reduce, include_self=include_self
)
return self
return None
@register_lowering(aten.scatter_, type_promotion_kind=None)
def scatter_(self, dim: int, index, src, *, reduce: Optional[str] = None):
assert reduce in {None, "add", "multiply"}
fallback_result = scatter_fallback(
"aten.scatter_", self, dim, index, src, reduce=reduce
)
if fallback_result:
return fallback_result
if reduce == "add":
reduce = "sum"
elif reduce == "multiply":
reduce = "prod"
return scatter_reduce_(self, dim, index, src, reduce)
@register_lowering(aten.scatter_add, type_promotion_kind=None)
def scatter_add(x, dim: int, index, src):
return scatter_add_(clone(x), dim, index, src)
@register_lowering(aten.scatter_add_, type_promotion_kind=None)
def scatter_add_(x, dim: int, index, src):
return scatter_reduce_(x, dim, index, src, "sum")
@register_lowering(aten.scatter_reduce, type_promotion_kind=None)
def scatter_reduce(x, dim: int, index, src, reduction_type, **kwargs):
return scatter_reduce_(clone(x), dim, index, src, reduction_type, **kwargs)
@register_lowering(aten.scatter_reduce_, type_promotion_kind=None)
def scatter_reduce_(self, dim: int, index, src, reduce, *, include_self: bool = True):
assert reduce in {None, "sum", "prod", "mean", "amax", "amin"}
fallback_result = scatter_fallback(
"aten.scatter_reduce_",
self,
dim,
index,
src,
reduce=reduce,
include_self=include_self,
)
if fallback_result:
return fallback_result
assert isinstance(self, TensorBox)
assert "int" in str(index.get_dtype())
ndim = len(self.get_size())
if ndim == 0:
self = view(self, [1])
if isinstance(src, TensorBox) and len(src.get_size()) == 0:
src = view(src, [1])
if isinstance(index, TensorBox) and len(index.get_size()) == 0:
index = view(index, [1])
dim = _validate_dim(self, dim)
self.realize()
index_loader = index.make_loader()
src_loader = src.make_loader() if isinstance(src, TensorBox) else None
def output_indexer(idx):
# self is captured from the end of the function, so it may have 0 dim
shape = self.get_size()
ndim = len(shape)
indirect_idx = list(idx)
indirect_idx[dim] = ops.indirect_indexing(
index_loader(idx), 1 if ndim == 0 else shape[dim]
)
return indirect_idx
def fn(idx):
if src_loader:
return src_loader(idx)
else:
# src is a scalar
return ops.constant(src, self.get_dtype())
def backend_reduce_str(reduce):
if reduce == "sum":
return "atomic_add"
else:
# TODO: Need to support more reduction type
assert reduce is None
return None
if not include_self:
# zero out the corresponding elements first
zero_out = ir.Scatter(
device=self.get_device(),
dtype=self.get_dtype(),
inner_fn=lambda index: ops.constant(0, self.get_dtype()),
ranges=index.get_size(),
output_indexer=output_indexer,
scatter_mode=None,
)
buffer = ir.ComputedBuffer(
None,
ir.MutationLayout(self),
zero_out,
)
buffer.name = V.graph.register_buffer(buffer)
# self[index[i][j][k]][j][k] += src[i][j][k] # if dim == 0
# self[i][index[i][j][k]][k] += src[i][j][k] # if dim == 1
# self[i][j][index[i][j][k]] += src[i][j][k] # if dim == 2
scatter = ir.Scatter(
device=self.get_device(),
dtype=self.get_dtype(),
inner_fn=fn,
ranges=index.get_size(),
output_indexer=output_indexer,
scatter_mode=backend_reduce_str(reduce),
)
buffer = ir.ComputedBuffer(
None,
ir.MutationLayout(self),
scatter,
)
buffer.name = V.graph.register_buffer(buffer)
if ndim == 0:
self = view(self, [])
return self
def upsample_nearestnd(
x, output_size, scales_x: Tuple[Optional[float], ...], n: int = 2
):
x.realize_hint() # elements are reused
x_loader = x.make_loader()
i_sizes = x.get_size()[-n:]
batch = x.get_size()[:-n]
i_sizes = [V.graph.sizevars.evaluate_static_shape(i) for i in i_sizes]
assert len(scales_x) == n
o_sizes = output_size
scales = [i / o for i, o in zip(i_sizes, o_sizes)]
for i, scale in enumerate(scales):
if scale:
scales[i] = scale
def scale_fn(x, scale, size):
x = ops.index_expr(x, torch.float32)
x = ops.mul(x, ops.constant(scale, torch.float32))
x = ops.to_dtype(x, torch.int32)
return ops.indirect_indexing(x, size, check=False)
def fn(idx):
x = idx[-n:]
b = idx[:-n]
return x_loader(
[*b, *[scale_fn(i, s, size) for i, s, size in zip(x, scales, i_sizes)]]
)
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=fn,
ranges=[*batch, *o_sizes],
)
@register_lowering(aten.upsample_nearest1d.default)
def upsample_nearest1d(x, output_size, scales: Optional[float] = None):
return upsample_nearestnd(x, output_size, (scales,), n=1)
@register_lowering(aten.upsample_nearest2d.default)
def upsample_nearest2d(
x, output_size, scales_h: Optional[float] = None, scales_w: Optional[float] = None
):
return upsample_nearestnd(x, output_size, (scales_h, scales_w), n=2)
@register_lowering(aten.upsample_nearest3d.default)
def upsample_nearest3d(
x,
output_size,
scales_d: Optional[float] = None,
scales_h: Optional[float] = None,
scales_w: Optional[float] = None,
):
return upsample_nearestnd(x, output_size, (scales_d, scales_h, scales_w), n=3)
def _create_constants(*args, dtype):
return tuple(ops.constant(a, dtype) for a in args)
@register_lowering(aten.upsample_bicubic2d.default)
def upsample_bicubic2d_default(
x,
output_size,
align_corners: bool,
scales_h: Optional[float] = None,
scales_w: Optional[float] = None,
):
x.realize_hint()
x_loader = x.make_loader()
N, C, iH, iW = x.get_size()
oH, oW = output_size
iH = V.graph.sizevars.evaluate_static_shape(iH)
iW = V.graph.sizevars.evaluate_static_shape(iW)
def get_int_dtype(maxval):
if maxval > torch.iinfo(torch.int32).max:
return torch.int64
return torch.int32
def compute_scale(in_size, out_size, align_corners, scale=None):
if align_corners:
return (in_size - 1) / (out_size - 1) if out_size > 1 else 0
else:
return 1 / scale if scale is not None and scale > 0 else in_size / out_size
def compute_source_index(scale, dst_index, align_corners):
dst_index_ie = ops.index_expr(dst_index, torch.float32)
scale = ops.constant(scale, torch.float32)
if align_corners:
return ops.mul(scale, dst_index_ie)
else:
half = ops.constant(0.5, torch.float32)
return scale * (dst_index_ie + half) - half
def cubic_convolution1(x, A):
_Ap2, _Ap3, _1 = _create_constants(A + 2, A + 3, 1, dtype=torch.float32)
return (_Ap2 * x - _Ap3) * x * x + _1
def cubic_convolution2(x, A):
_A, _4A, _5A, _8A = _create_constants(
A, 4 * A, 5 * A, 8 * A, dtype=torch.float32
)
return ((_A * x - _5A) * x + _8A) * x - _4A
def get_cubic_upsample_coefficients(t):
A = -0.75
_1 = ops.constant(1.0, torch.float32)
c0 = cubic_convolution2(ops.add(t, _1), A)
c1 = cubic_convolution1(t, A)
x2 = ops.sub(_1, t)
c2 = cubic_convolution1(x2, A)
c3 = cubic_convolution2(ops.add(x2, _1), A)
return (c0, c1, c2, c3)
def cubic_interp1d(xs, t):
cs = get_cubic_upsample_coefficients(t)
# dot product between xs and cs
return xs[0] * cs[0] + xs[1] * cs[1] + xs[2] * cs[2] + xs[3] * cs[3]
height_scale = compute_scale(iH, oH, align_corners, scales_h)
width_scale = compute_scale(iW, oW, align_corners, scales_h)
def clamp(v, min, max):
return ops.maximum(min, ops.minimum(max, v))
def fn(idx):
n, c, oy, ox = idx
real_x = compute_source_index(width_scale, ox, align_corners)
in_x = ops.floor(real_x)
t_x = ops.sub(real_x, in_x)
real_y = compute_source_index(height_scale, oy, align_corners)
in_y = ops.floor(real_y)
t_y = ops.sub(real_y, in_y)
def load_bounded(fy, fx):
# TODO(Lezcano) Here we may not need to set-up a device_size
_0 = ops.constant(0, torch.int32)
iHm1 = ops.constant(iH - 1, torch.int32)
iWm1 = ops.constant(iW - 1, torch.int32)
iy = ops.indirect_indexing(clamp(fy, _0, iHm1), iH, check=False)
ix = ops.indirect_indexing(clamp(fx, _0, iWm1), iW, check=False)
return x_loader([n, c, iy, ix])
iy = ops.to_dtype(in_y, get_int_dtype(iH + 1))
ix = ops.to_dtype(in_x, get_int_dtype(iW + 1))
iys_ofs = tuple(ops.add(iy, ofs) for ofs in (-1, 0, 1, 2))
ixs_ofs = tuple(ops.add(ix, ofs) for ofs in (-1, 0, 1, 2))
def get_x_interp(y):
coeffs_x = tuple(load_bounded(y, x) for x in ixs_ofs)
return cubic_interp1d(coeffs_x, t_x)
coeffs_y = tuple(get_x_interp(y) for y in iys_ofs)
return cubic_interp1d(coeffs_y, t_y)
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=fn,
ranges=[N, C, sympy.Integer(oH), sympy.Integer(oW)],
)
@register_lowering(aten.reflection_pad2d)
def reflection_pad2d(x, padding):
assert len(padding) == 4
left, right, top, bot = padding
x_loader = x.make_loader()
*batch, h, w = x.get_size()
h = V.graph.sizevars.evaluate_static_shape(h)
w = V.graph.sizevars.evaluate_static_shape(w)
def reflect(x, size, offset):
size_num = size
size = ops.constant(size - 1, torch.int32)
x = ops.index_expr(x, torch.int32)
x = ops.sub(x, ops.constant(offset, torch.int32))
x = ops.sub(size, ops.abs(ops.sub(size, ops.abs(x))))
return ops.indirect_indexing(x, size_num, check=False)
def fn(idx):
*b, x, y = idx
x = reflect(x, h, top)
y = reflect(y, w, left)
return x_loader([*b, x, y])
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=fn,
ranges=[*batch, sympy.Integer(h + top + bot), sympy.Integer(w + left + right)],
)
@register_lowering(aten.reflection_pad2d_backward)
def reflection_pad2d_backward(grad_output, x, padding):
assert len(padding) == 4
left, right, top, bot = padding
*_, h, w = x.get_size()
h = V.graph.sizevars.evaluate_static_shape(h) - 1
w = V.graph.sizevars.evaluate_static_shape(w) - 1
grad_loader = grad_output.make_loader()
*_, h_grad, w_grad = grad_output.get_size()
def fn(idx):
*b, x, y = idx
def load_from_output(x, y):
return grad_loader([*b, x, y])
def index_range_condition(index_range):
i, lb, ub = index_range
i = ops.index_expr(i, torch.int32)
lb = ops.index_expr(lb, torch.int64)
ub = ops.index_expr(ub, torch.int64)
return ops.and_(ops.ge(i, lb), ops.le(i, ub))
# Areas after reflection:
#
# top-left | top | top-right
# -----------------------------------------
# left | center | right
# -----------------------------------------
# bottom-left | bottom | bottom-right
#
# The center area is the original matrix. Other areas are reflections.
center_x, center_y = x + top, y + left
top_reflect_x, left_reflect_y = top - x, left - y
bot_reflect_x, right_reflect_y = 2 * h + top - x, 2 * w + left - y
# Accumulate gradients from different areas
# If some of the padding is negative, center load is not always valid
range_cx = (center_x, 0, h + top + bot)
range_cy = (center_y, 0, w + left + right)
cond = ops.and_(
index_range_condition(range_cx), index_range_condition(range_cy)
)
grad = ops.masked(cond, lambda: load_from_output(center_x, center_y), 0.0)
def accumulate(out_x, out_y, index_range1, index_range2=None):
nonlocal grad
# If the upper bound is less than the lower bound, we can get rid of one accumulation.
# This happens when the padding size is zero.
upper_less_than_lower1 = index_range1[2] < index_range1[1]
if isinstance(upper_less_than_lower1, bool) and upper_less_than_lower1:
return
cond = index_range_condition(index_range1)
if index_range2 is not None:
upper_less_than_lower2 = index_range2[2] < index_range2[1]
if isinstance(upper_less_than_lower2, bool) and upper_less_than_lower2:
return
cond = ops.and_(cond, index_range_condition(index_range2))
g = ops.masked(cond, lambda: load_from_output(out_x, out_y), 0.0)
grad = ops.add(grad, g)
accumulate(center_x, left_reflect_y, range_cx, (y, 1, left))
accumulate(center_x, right_reflect_y, range_cx, (y, w - right, w - 1))
accumulate(top_reflect_x, center_y, (x, 1, top), range_cy)
accumulate(bot_reflect_x, center_y, (x, h - bot, h - 1), range_cy)
accumulate(top_reflect_x, left_reflect_y, (x, 1, top), (y, 1, left))
accumulate(top_reflect_x, right_reflect_y, (x, 1, top), (y, w - right, w - 1))
accumulate(bot_reflect_x, left_reflect_y, (x, h - bot, h - 1), (y, 1, left))
accumulate(
bot_reflect_x, right_reflect_y, (x, h - bot, h - 1), (y, w - right, w - 1)
)
return grad
return Pointwise.create(
device=grad_output.get_device(),
dtype=grad_output.get_dtype(),
inner_fn=fn,
ranges=list(x.get_size()),
)
@register_lowering(prims.rev.default)
def rev(x, dims):
# note - dims pre-canonicalized
x_loader = x.make_loader()
sizes = x.get_size()
def loader(idx):
idx = list(idx)
assert len(idx) == len(sizes)
for dim in dims:
idx[dim] = (sizes[dim] - 1) - idx[dim]
return x_loader(idx)
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=loader,
ranges=sizes,
)
@register_lowering(aten.constant_pad_nd, type_promotion_kind=None)
def constant_pad_nd(x, padding, fill_value=0):
assert (len(padding) % 2) == 0
if all(p == 0 for p in padding):
return clone(x)
sizes = x.get_size()
bounds = list(reversed(list(zip(padding[::2], padding[1::2]))))
n = len(sizes) - len(bounds)
# if padding is a complicated expression, hoist it
bounds_precomp = []
for l, h in bounds:
l_precomp = (
V.graph.sizevars.lookup_precomputed_size(l)
if isinstance(l, sympy.Expr) and l.free_symbols
else l
)
bounds_precomp.append((l_precomp, h))
output_size = list(sizes[:n])
mask_sizes = []
for (low, high), size in zip(bounds, sizes[n:]):
mask_sizes.append(size)
output_size.append(sympy.expand(size + low + high))
assert len(output_size) == len(sizes)
fill_value = dtype_to_type(x.get_dtype())(fill_value)
def mask(index):
mask = []
for idx, (low, high), length in zip(index[n:], bounds, mask_sizes):
if low != 0:
mask.append(range_mask_low(idx, 0))
if high != 0:
mask.append(range_mask_high(idx, length))
mask = functools.reduce(ops.and_, mask)
return ops.masked(mask, lambda: x_loader(index), fill_value)
def offset_fn(index):
new_index = list(index[:n])
for idx, (low, high) in zip(index[n:], bounds_precomp):
new_index.append(idx - low)
assert len(new_index) == len(index)
return mask(new_index)
x_loader = x.make_loader()
return Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=offset_fn,
ranges=output_size,
)
def range_mask_low(i: sympy.Expr, low: Union[sympy.Expr, int]):
return ops.ge(
ops.index_expr(i, torch.int64),
ops.index_expr(sympy.Integer(low), torch.int64),
)
def range_mask_high(i: sympy.Expr, high: sympy.Expr):
return ops.lt(
ops.index_expr(i, torch.int64),
ops.index_expr(high, torch.int64),
)
def range_mask(i: sympy.Expr, high: sympy.Expr, low: sympy.Expr):
return ops.and_(
range_mask_low(i, low),
range_mask_high(i, high),
)
def constant_boundary_condition_2d(x, fill_value, padding=None, pad_fill_value=1.0):
*_, h, w = x.get_size()
x_loader = x.make_loader()
padding_h = padding[0] if padding else 0
padding_w = padding[1] if padding else 0
def load(index):
*prefix, ih, iw = index
mask = ops.and_(
range_mask(ih, h + padding_h, -padding_h),
range_mask(iw, w + padding_w, -padding_w),
)
return (
ops.masked(
mask,
lambda: constant_boundary_condition_2d(x, pad_fill_value)(
[*prefix, ih, iw]
),
fill_value,
)
if padding
else ops.masked(mask, lambda: x_loader([*prefix, ih, iw]), fill_value)
)
return load
def pooling_size(x, i, kernel_size, stride, padding, ceil_mode):
x_out = FloorDiv(
x + 2 * padding[i] - (kernel_size[i] - 1) + (stride[i] - 1), stride[i]
)
if ceil_mode:
x_alt = FloorDiv(
x + 2 * padding[i] - (kernel_size[i] - 1) + 2 * (stride[i] - 1), stride[i]
)
if V.graph.sizevars.size_hint((x_alt - 1) * stride[i] - x - padding[i]) >= 0:
# Sliding windows must start within the input or left padding
x_alt -= 1
V.graph.sizevars.guard_leq(0, x_alt * stride[i] - x - padding[i])
if V.graph.sizevars.size_hint(x_out - x_alt) == 0:
# ceil mode is actually a no-op, lets guard on that
V.graph.sizevars.guard_equals(x_out, x_alt)
ceil_mode = False
else:
x_out = x_alt
return x_out, ceil_mode
fallback_max_pool2d_with_indices = fallback_handler(aten.max_pool2d_with_indices)
@register_lowering(aten.max_pool2d_with_indices, type_promotion_kind=None)
def max_pool2d_with_indices(
x, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False
):
if padding == 0:
padding = [0, 0]
if dilation == 1:
dilation = [1, 1]
if not stride:
stride = kernel_size
kernel_size = pad_listlike(kernel_size, 2)
stride = pad_listlike(stride, 2)
padding = pad_listlike(padding, 2)
dilation = pad_listlike(dilation, 2)
assert isinstance(x, TensorBox)
assert len(kernel_size) == 2
assert len(stride) == 2
assert len(padding) == 2
assert len(dilation) == 2
assert len(x.get_size()) in (3, 4)
x.realize_hint()
*batch, h, w = x.get_size()
h_out, ceil_mode1 = pooling_size(h, 0, kernel_size, stride, padding, ceil_mode)
w_out, ceil_mode2 = pooling_size(w, 1, kernel_size, stride, padding, ceil_mode)
if padding[0] or padding[1] or ceil_mode1 or ceil_mode2:
x_loader = constant_boundary_condition_2d(x, float("-inf"))
else:
x_loader = x.make_loader()
new_size = list(batch) + [h_out, w_out]
window_size = kernel_size[0] * kernel_size[1]
if window_size > 25 or any(d != 1 for d in dilation):
# Kernel size too big. Results in hard-to-optimize Triton code. Use fallback.
return fallback_max_pool2d_with_indices(
x, kernel_size, stride, padding, dilation, ceil_mode
)
def fn(idx, return_index):
*prefix, bh, bw = idx
maxval = None
maxindex = None
for ih, iw in itertools.product(range(kernel_size[0]), range(kernel_size[1])):
ih = bh * stride[0] + ih - padding[0]
iw = bw * stride[1] + iw - padding[1]
val = x_loader([*prefix, ih, iw])
if return_index:
index = ops.index_expr(ih * w + iw, torch.int64)
if maxindex is None:
maxindex = index
else:
maxindex = ops.where(ops.gt(val, maxval), index, maxindex)
if maxval is None:
maxval = val
else:
maxval = ops.maximum(val, maxval)
if return_index:
return maxindex
else:
return maxval
r1 = Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=functools.partial(fn, return_index=False),
ranges=new_size,
)
r2 = Pointwise.create(
device=x.get_device(),
dtype=torch.int64,
inner_fn=functools.partial(fn, return_index=True),
ranges=new_size,
)
# TODO(jansel): should we force these to be realized?
return r1, r2
fallback_max_pool2d_with_indices_backward = fallback_handler(
aten.max_pool2d_with_indices_backward
)
@register_lowering(aten.max_pool2d_with_indices_backward, type_promotion_kind=None)
def max_pool2d_with_indices_backward(
grad_output, x, kernel_size, stride, padding, dilation, ceil_mode, indices
):
if padding == 0:
padding = [0, 0]
if dilation == 1:
dilation = [1, 1]
if not stride:
stride = kernel_size
assert isinstance(x, TensorBox)
assert len(kernel_size) == 2
assert len(stride) == 2
assert len(padding) == 2
assert len(dilation) == 2
assert len(x.get_size()) in (3, 4)
# we will read this many times, so make sure it is computed
grad_output.realize_hint()
try:
gO_stride = grad_output.get_stride()
except AttributeError:
# some classes don't have `get_stride`
# TODO will need a better way of determining if inputs are channels-last
gO_stride = None
if isinstance(x, TensorBox) and isinstance(x.data.data, Pointwise): # type: ignore[attr-defined]
data = x.data.data # type: ignore[attr-defined]
x_buffer = ir.ComputedBuffer(
name=None,
layout=ir.FlexibleLayout(
device=data.get_device(),
dtype=data.get_dtype(),
size=data.get_size(),
),
data=data,
)
x_buffer.decide_layout()
x_stride = x_buffer.get_stride()
else:
try:
x_stride = x.get_stride()
except AttributeError:
x_stride = None
is_channels_last = (x_stride is not None and x_stride[1] == 1) or (
gO_stride is not None and gO_stride[1] == 1
)
autotune = (
config.coordinate_descent_tuning
or config.max_autotune
or config.max_autotune_pointwise
)
if any(d != 1 for d in dilation) or (is_channels_last and not autotune):
# don't codegen channels-last when autotune is not enabled, it's very slow
return fallback_max_pool2d_with_indices_backward(
grad_output, x, kernel_size, stride, padding, dilation, ceil_mode, indices
)
indices.realize_hint()
*batch, height, width = x.get_size()
*_, pooled_height, pooled_width = grad_output.get_size()
indices_loader = indices.make_loader()
grad_loader = grad_output.make_loader()
new_size = list(x.get_size())
h_window_size = max(
[
max(h // stride[0] - max(0, (h - kernel_size[0]) // stride[0]), 1)
for h in range(kernel_size[0] * 2)
]
)
w_window_size = max(
[
max(w // stride[1] - max(0, (w - kernel_size[1]) // stride[1]), 1)
for w in range(kernel_size[1] * 2)
]
)
window_size = h_window_size * w_window_size
if window_size > 25:
# Kernel size too big. Results in hard-to-optimize Triton code. Use fallback.
return fallback_max_pool2d_with_indices_backward(
grad_output, x, kernel_size, stride, padding, dilation, ceil_mode, indices
)
indices_size = indices.get_size()
def fn(idx):
*prefix, h, w = idx
index_test = ops.index_expr(h * width + w, torch.int32)
h = h + padding[0]
w = w + padding[1]
phstart = ops.index_expr(
FloorDiv(h - kernel_size[0] + stride[0], stride[0]), torch.int32
)
pwstart = ops.index_expr(
FloorDiv(w - kernel_size[1] + stride[1], stride[1]), torch.int32
)
phend = ops.index_expr(FloorDiv(h, stride[0]) + 1, torch.int32)
pwend = ops.index_expr(FloorDiv(w, stride[1]) + 1, torch.int32)
phstart = ops.maximum(phstart, ops.constant(0, torch.int32))
pwstart = ops.maximum(pwstart, ops.constant(0, torch.int32))
phend = ops.minimum(phend, ops.index_expr(pooled_height, torch.int32))
pwend = ops.minimum(pwend, ops.index_expr(pooled_width, torch.int32))
gradient = None
for ph_ in range(h_window_size):
for pw_ in range(w_window_size):
ph = ops.add(phstart, ops.constant(ph_, torch.int32))
pw = ops.add(pwstart, ops.constant(pw_, torch.int32))
grad_index = [
*prefix,
ops.indirect_indexing(
ops.minimum(ph, ops.sub(phend, ops.constant(1, torch.int32))),
indices_size[-2],
check=False,
),
ops.indirect_indexing(
ops.minimum(pw, ops.sub(pwend, ops.constant(1, torch.int32))),
indices_size[-1],
check=False,
),
]
index_actual = indices_loader(grad_index)
grad_part = grad_loader(grad_index)
check = ops.eq(index_actual, index_test)
if gradient is None:
# don't need mask for 0, 0
gradient = ops.where(
check, grad_part, ops.constant(0.0, torch.float32)
)
else:
mask = ops.and_(
ops.and_(
ops.lt(ph, phend),
ops.lt(pw, pwend),
),
check,
)
gradient = ops.where(mask, ops.add(gradient, grad_part), gradient)
assert gradient is not None
return gradient
return Pointwise.create(
device=grad_output.get_device(),
dtype=grad_output.get_dtype(),
inner_fn=fn,
ranges=new_size,
)
def pad_adaptive_loader(x):
*_, h, w = x.get_size()
x_loader = x.make_loader()
def load(prefix, increments, start_indices, end_indices):
ih, iw = increments
h_start_index, w_start_index = start_indices
h_end_index, w_end_index = end_indices
mask = ops.and_(
ops.lt(
ops.index_expr(h_start_index + ih, torch.int64),
ops.index_expr(h_end_index, torch.int64),
),
ops.lt(
ops.index_expr(w_start_index + iw, torch.int64),
ops.index_expr(w_end_index, torch.int64),
),
)
return ops.masked(
mask,
lambda: x_loader([*prefix, h_start_index + ih, w_start_index + iw]),
0.0,
)
return load
def _adaptive_pooling_idx_sum(kernel_maxes, start_index_fns, end_index_fns):
h_start_index_fn, w_start_index_fn = start_index_fns
h_end_index_fn, w_end_index_fn = end_index_fns
def fn_sum(idx, loader):
*prefix, bh, bw = idx
h_start_index = h_start_index_fn(bh)
h_end_index = h_end_index_fn(bh)
w_start_index = w_start_index_fn(bw)
w_end_index = w_end_index_fn(bw)
total = None
for ih, iw in itertools.product(range(kernel_maxes[0]), range(kernel_maxes[1])):
val = loader(
prefix,
[ih, iw],
[h_start_index, w_start_index],
[h_end_index, w_end_index],
)
if total is None:
total = val
else:
total = ops.add(val, total)
return total
return fn_sum
fallback_adaptive_avg_pool2d = fallback_handler(aten._adaptive_avg_pool2d)
@register_lowering(aten._adaptive_avg_pool2d)
def _adaptive_avg_pool2d(x, output_size):
assert isinstance(x, TensorBox)
assert len(output_size) == 2
x.realize_hint()
*batch, h_in, w_in = x.get_size()
h_in = V.graph.sizevars.evaluate_static_shape(h_in)
w_in = V.graph.sizevars.evaluate_static_shape(w_in)
h_out, w_out = output_size
# no-op if the same input and output
if h_in == h_out and w_in == w_out:
return clone(x)
if h_out == 0 or w_out == 0:
o_size = [*batch, h_out, w_out]
return empty(o_size, dtype=x.get_dtype(), device=x.get_device())
if h_in % h_out == 0 and w_in % w_out == 0:
kernel_size = [h_in // h_out, w_in // w_out]
return avg_pool2d(x, kernel_size)
h_kernel_max = ceildiv((h_in + h_out - 1), h_out)
w_kernel_max = ceildiv((w_in + w_out - 1), w_out)
new_size = list(batch) + [h_out, w_out]
dtype = x.get_dtype()
def start_index(index, out_dim, inp_dim):
return FloorDiv((index * inp_dim), out_dim)
def end_index(index, out_dim, inp_dim):
return FloorDiv((index + 1) * inp_dim + out_dim - 1, out_dim)
h_start_index = functools.partial(start_index, out_dim=h_out, inp_dim=h_in)
h_end_index = functools.partial(end_index, out_dim=h_out, inp_dim=h_in)
w_start_index = functools.partial(start_index, out_dim=w_out, inp_dim=w_in)
w_end_index = functools.partial(end_index, out_dim=w_out, inp_dim=w_in)
window_size = h_kernel_max * w_kernel_max
if window_size > 25:
# Kernel size too big. Results in hard-to-optimize Triton code. Use fallback.
return fallback_adaptive_avg_pool2d(x, output_size)
fn_sum = _adaptive_pooling_idx_sum(
[h_kernel_max, w_kernel_max],
[h_start_index, w_start_index],
[h_end_index, w_end_index],
)
ones_loader = pad_adaptive_loader(ones_like(x))
def fn(idx):
return ops.div(fn_sum(idx, pad_adaptive_loader(x)), fn_sum(idx, ones_loader))
rv = Pointwise.create(
device=x.get_device(),
dtype=dtype,
inner_fn=fn,
ranges=new_size,
)
# TODO: should we force these to be realized?
return rv
@register_lowering(aten.upsample_nearest2d_backward.default)
def upsample_nearest2d_backward(
x, output_size=None, input_size=None, scales_h=None, scales_w=None
):
x.realize_hint()
*batch, inp_h, inp_w = x.get_size()
inp_h = V.graph.sizevars.evaluate_static_shape(inp_h)
inp_w = V.graph.sizevars.evaluate_static_shape(inp_w)
*batch, out_h, out_w = input_size
if inp_h % out_h == 0 and inp_w % out_w == 0:
return avg_pool2d(x, [inp_h // out_h, inp_w // out_w], divisor_override=1)
h_kernel_max = ceildiv(inp_h, out_h)
w_kernel_max = ceildiv(inp_w, out_w)
def start_index(index, out_dim, inp_dim):
return CeilDiv(index * inp_dim, out_dim)
def end_index(index, out_dim, inp_dim):
return start_index((index + 1), out_dim, inp_dim)
h_start_index = functools.partial(start_index, out_dim=out_h, inp_dim=inp_h)
h_end_index = functools.partial(end_index, out_dim=out_h, inp_dim=inp_h)
w_start_index = functools.partial(start_index, out_dim=out_w, inp_dim=inp_w)
w_end_index = functools.partial(end_index, out_dim=out_w, inp_dim=inp_w)
fn_sum = _adaptive_pooling_idx_sum(
[h_kernel_max, w_kernel_max],
[h_start_index, w_start_index],
[h_end_index, w_end_index],
)
def fn(idx):
return fn_sum(idx, pad_adaptive_loader(x))
rv = Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=fn,
ranges=list(input_size),
)
return rv
fallback_avg_pool2d = fallback_handler(aten.avg_pool2d)
@register_lowering(aten.avg_pool2d, type_promotion_kind=None)
def avg_pool2d(
x,
kernel_size,
stride=(),
padding=0,
ceil_mode=False,
count_include_pad=True,
divisor_override=None,
):
if not stride:
stride = kernel_size
if not padding:
padding = [0, 0]
kernel_size = pad_listlike(kernel_size, 2)
stride = pad_listlike(stride, 2)
padding = pad_listlike(padding, 2)
assert isinstance(x, TensorBox)
assert len(kernel_size) == 2
assert len(stride) == 2
assert len(padding) == 2
assert len(x.get_size()) in (3, 4)
x.realize_hint()
*batch, h, w = x.get_size()
h_out, ceil_mode1 = pooling_size(h, 0, kernel_size, stride, padding, ceil_mode)
w_out, ceil_mode2 = pooling_size(w, 1, kernel_size, stride, padding, ceil_mode)
if padding[0] or padding[1] or ceil_mode1 or ceil_mode2:
x_loader = constant_boundary_condition_2d(x, 0.0)
had_padding = True
else:
x_loader = x.make_loader()
had_padding = False
new_size = list(batch) + [h_out, w_out]
dtype = x.get_dtype()
window_size = kernel_size[0] * kernel_size[1]
if window_size > 25:
# Kernel size too big. Results in hard-to-optimize Triton code. Use fallback.
return fallback_avg_pool2d(
x,
kernel_size,
stride,
padding,
ceil_mode,
count_include_pad,
divisor_override,
)
def fn_sum(idx, loader):
*prefix, bh, bw = idx
total = None
for ih, iw in itertools.product(range(kernel_size[0]), range(kernel_size[1])):
ih = bh * stride[0] + ih - padding[0]
iw = bw * stride[1] + iw - padding[1]
val = loader([*prefix, ih, iw])
if total is None:
total = val
else:
total = ops.add(val, total)
return total
if not had_padding or divisor_override:
if divisor_override:
scale = 1 / divisor_override
else:
scale = 1.0 / (kernel_size[0] * kernel_size[1])
def fn(idx):
return ops.mul(fn_sum(idx, x_loader), ops.constant(scale, dtype))
else:
ones_loader = constant_boundary_condition_2d(
ones_like(x), 0.0, padding if count_include_pad else None
)
def fn(idx):
# TODO(jansel): optimize to do `int(x<h)` rather than `x<h?1:0`
return ops.div(fn_sum(idx, x_loader), fn_sum(idx, ones_loader))
rv = Pointwise.create(
device=x.get_device(),
dtype=dtype,
inner_fn=fn,
ranges=new_size,
)
# TODO(jansel): should we force these to be realized?
return rv
fallback_avg_pool2d_backward = fallback_handler(aten.avg_pool2d_backward)
@register_lowering(aten.avg_pool2d_backward, type_promotion_kind=None)
def avg_pool2d_backward(
grad_output,
x,
kernel_size,
stride,
padding,
ceil_mode,
count_include_pad,
divisor_override=None,
):
assert divisor_override is None or divisor_override != 0, "divisor must be not zero"
if not stride:
stride = kernel_size
if not padding:
padding = [0, 0]
assert isinstance(grad_output, TensorBox)
assert isinstance(x, TensorBox)
assert len(kernel_size) == 2
assert len(stride) == 2
assert len(padding) == 2
assert len(x.get_size()) in (3, 4)
grad_output.realize_hint() # we will read this many times, so make sure it is computed
*batch, height, width = x.get_size()
h_out, ceil_mode1 = pooling_size(height, 0, kernel_size, stride, padding, ceil_mode)
w_out, ceil_mode2 = pooling_size(width, 1, kernel_size, stride, padding, ceil_mode)
grad_loader = grad_output.make_loader()
had_padding = padding[0] or padding[1] or ceil_mode1 or ceil_mode2
*_, pooled_height, pooled_width = grad_output.get_size()
new_size = list(x.get_size())
dtype = x.get_dtype()
h_window_size = max(
[
max(h // stride[0] - max(0, (h - kernel_size[0]) // stride[0]), 1)
for h in range(kernel_size[0] * 2)
]
)
w_window_size = max(
[
max(w // stride[1] - max(0, (w - kernel_size[1]) // stride[1]), 1)
for w in range(kernel_size[1] * 2)
]
)
window_size = h_window_size * w_window_size
if window_size > 25:
# Kernel size too big. Results in hard-to-optimize Triton code. Use fallback.
return fallback_avg_pool2d_backward(
grad_output,
x,
kernel_size,
stride,
padding,
ceil_mode,
count_include_pad,
divisor_override,
)
def compute_pool_size_without_padding(ph, pw):
"""
This computes the scaling factor that we will divide an element
by when `count_include_pad=False`
"""
stride_h = ops.constant(stride[0], torch.int32)
stride_w = ops.constant(stride[1], torch.int32)
pad_h = ops.constant(padding[0], torch.int32)
pad_w = ops.constant(padding[1], torch.int32)
kernel_h = ops.constant(kernel_size[0], torch.int32)
kernel_w = ops.constant(kernel_size[1], torch.int32)
hstart = ops.sub(ops.mul(ph, stride_h), pad_h)
wstart = ops.sub(ops.mul(pw, stride_w), pad_w)
hend = ops.minimum(
ops.add(hstart, kernel_h),
ops.add(ops.index_expr(height, torch.int32), pad_h),
)
wend = ops.minimum(
ops.add(wstart, kernel_w),
ops.add(ops.index_expr(width, torch.int32), pad_w),
)
hstart = ops.maximum(hstart, ops.constant(0, torch.int32))
wstart = ops.maximum(wstart, ops.constant(0, torch.int32))
hend = ops.minimum(hend, ops.index_expr(height, torch.int32))
wend = ops.minimum(wend, ops.index_expr(width, torch.int32))
divide_factor = ops.mul(ops.sub(hend, hstart), ops.sub(wend, wstart))
return divide_factor
def fn(idx):
*prefix, h, w = idx
h = h + padding[0]
w = w + padding[1]
phstart = ops.index_expr(
FloorDiv(h - kernel_size[0] + stride[0], stride[0]), torch.int32
)
pwstart = ops.index_expr(
FloorDiv(w - kernel_size[1] + stride[1], stride[1]), torch.int32
)
phend = ops.index_expr(FloorDiv(h, stride[0]) + 1, torch.int32)
pwend = ops.index_expr(FloorDiv(w, stride[1]) + 1, torch.int32)
phstart = ops.maximum(phstart, ops.constant(0, torch.int32))
pwstart = ops.maximum(pwstart, ops.constant(0, torch.int32))
phend = ops.minimum(phend, ops.index_expr(pooled_height, torch.int32))
pwend = ops.minimum(pwend, ops.index_expr(pooled_width, torch.int32))
gradient = None
for ph_ in range(h_window_size):
for pw_ in range(w_window_size):
ph = ops.add(phstart, ops.constant(ph_, torch.int32))
pw = ops.add(pwstart, ops.constant(pw_, torch.int32))
if divisor_override is not None:
scale = divisor_override
elif count_include_pad or not had_padding:
scale = kernel_size[0] * kernel_size[1]
else:
scale = compute_pool_size_without_padding(ph, pw)
part = ops.truediv(
grad_loader(
[
*prefix,
ops.indirect_indexing(
ops.minimum(
ph, ops.sub(phend, ops.constant(1, torch.int32))
),
pooled_height,
check=False,
),
ops.indirect_indexing(
ops.minimum(
pw, ops.sub(pwend, ops.constant(1, torch.int32))
),
pooled_width,
check=False,
),
]
),
scale,
)
mask = ops.and_(
ops.lt(ph, phend),
ops.lt(pw, pwend),
)
if gradient is None:
gradient = ops.where(mask, part, ops.constant(0.0, torch.float32))
else:
gradient = ops.where(mask, ops.add(gradient, part), gradient)
assert gradient is not None
return gradient
rv = Pointwise.create(
device=grad_output.get_device(),
dtype=dtype,
inner_fn=fn,
ranges=new_size,
)
return rv
def _validate_reduction_axis(x, axis):
size = x.get_size()
if isinstance(axis, int):
axis = [axis]
elif not axis:
axis = range(len(size))
if len(size) == 0:
assert tuple(axis) in [(), (0,), (-1,)], f"invalid axis: {axis}"
return []
axis = list(axis)
for i in range(len(axis)):
if axis[i] < 0:
axis[i] += len(size) if len(size) else 1
assert 0 <= axis[i] < len(size) or (len(size) == 0 and axis[i] == 0)
assert len(set(axis)) == len(axis), "reduction axis not unique"
return axis
def _make_reduction_inner(x, *, axis, keepdims, dtype, override_return_dtype):
if dtype is not None:
x = to_dtype(x, dtype)
size = x.get_size()
axis = set(_validate_reduction_axis(x, axis))
kept_sizes = []
kept_idx = []
reduced_sizes = []
reduced_idx = []
for i in range(len(size)):
if i in axis:
reduced_idx.append(i)
reduced_sizes.append(size[i])
else:
kept_idx.append(i)
kept_sizes.append(size[i])
def loader(index, reduction_index):
assert len(reduction_index) == len(reduced_idx)
if keepdims:
assert len(index) == len(size)
assert all(index[i] == 0 for i in reduced_idx)
index = [index[i] for i in kept_idx]
assert len(index) == len(kept_idx)
new_index = [None] * (len(index) + len(reduction_index))
for idx, var in itertools.chain(
zip(kept_idx, index), zip(reduced_idx, reduction_index)
):
new_index[idx] = var
return inner_loader(new_index)
if keepdims:
new_size = list(size)
for i in reduced_idx:
new_size[i] = sympy.Integer(1)
else:
new_size = kept_sizes
inner_loader = x.make_loader()
return dict(
device=x.get_device(),
dst_dtype=override_return_dtype or x.get_dtype(),
src_dtype=x.get_dtype(),
inner_fn=loader,
ranges=new_size,
reduction_ranges=reduced_sizes,
)
def make_reduction(reduction_type: str, override_return_dtype=None):
def inner(x, axis=None, keepdims=False, *, dtype=None):
kwargs = _make_reduction_inner(
x,
axis=axis,
keepdims=keepdims,
dtype=dtype,
override_return_dtype=override_return_dtype,
)
result = Reduction.create(reduction_type=reduction_type, **kwargs)
if isinstance(
result.data.data, Reduction
): # Only realize if reduction isn't unrolled
result.realize()
return result
return inner
@register_lowering(aten.mean)
def mean(x, axis=None, keepdim=False, *, dtype=None):
if dtype is not None:
x = to_dtype(x, dtype)
size = x.get_size()
axis = _validate_reduction_axis(x, axis)
# compute in higher-precision until end of mean lowering
output_dtype = x.get_dtype()
if output_dtype in (torch.float16, torch.bfloat16):
x = to_dtype(x, torch.float)
sum_result = sum_(x, axis, keepdim)
denom = sympy_product(size[i] for i in axis)
denom = ir.IndexingConstant(denom, x.get_dtype(), x.get_device())
denom = ExpandView.create(denom, list(sum_result.get_size()))
return to_dtype(div(sum_result, denom), output_dtype)
def var_mean_sum_(x, axis, correction, keepdim, return_mean):
if correction is None:
correction = 1
size = x.get_size()
axis = _validate_reduction_axis(x, axis)
x_mean = mean(x, axis, keepdim=True)
if return_mean:
x_mean.realize()
diffs = square(sub(x, x_mean))
sum_result = sum_(diffs, axis, keepdim)
denom = sympy_product(size[i] for i in axis)
if correction:
denom = denom - correction
denom = ir.IndexingConstant(denom, x.get_dtype(), x.get_device())
denom = ExpandView.create(denom, list(sum_result.get_size()))
x_var = div(sum_result, denom)
if not return_mean:
return x_var
x_mean = x_mean if keepdim else squeeze(x_mean, axis)
return x_var, x_mean
def use_two_step_variance(x, axis, keepdim):
# Instead of unrolling welford, just unroll the simpler two-step var
axis = _validate_reduction_axis(x, axis)
kwargs = _make_reduction_inner(
x, axis=axis, keepdims=keepdim, dtype=None, override_return_dtype=None
)
ranges = kwargs["ranges"]
reduction_numel = sympy_product(kwargs["reduction_ranges"])
return (
isinstance(reduction_numel, sympy.Integer)
and int(reduction_numel) < config.unroll_reductions_threshold
and sympy_product(ranges) != 1
)
def var_mean_welford_(x, axis, *, correction, keepdim, return_mean):
if correction is None:
correction = 1
kwargs = _make_reduction_inner(
x, axis=axis, keepdims=keepdim, dtype=None, override_return_dtype=None
)
loader = kwargs.pop("inner_fn")
kwargs.pop("dst_dtype")
kwargs.pop("src_dtype")
mean, m2, _ = ir.WelfordReduction.create(
inner_fns=(loader,),
reduction_type="welford_reduce",
dtype=x.get_dtype(),
**kwargs,
)
m2.realize()
dtype = x.get_dtype()
size = x.get_size()
axis = _validate_reduction_axis(x, axis)
rnumel = sympy_product(size[i] for i in axis)
def get_constant_or_index_expr(x, dtype):
if isinstance(x, sympy.Expr) and not x.is_constant():
return ops.to_dtype(ops.index_expr(x, torch.int64), dtype)
return ops.constant(x, dtype)
def scale_fn(data):
c = get_constant_or_index_expr(correction, dtype)
N = get_constant_or_index_expr(rnumel, dtype)
return data / (N - c)
var = make_pointwise(scale_fn)(m2)
if return_mean:
mean.realize()
return var, mean
return var
@register_lowering([aten.var, prims.var])
def var_(x, axis=None, *, correction=None, keepdim=False):
if use_two_step_variance(x, axis=axis, keepdim=keepdim):
return var_mean_sum_(
x, axis=axis, correction=correction, keepdim=keepdim, return_mean=False
)
return var_mean_welford_(
x, axis=axis, correction=correction, keepdim=keepdim, return_mean=False
)
@register_lowering(aten.var_mean)
def var_mean(x, axis=None, *, correction=None, keepdim=False):
if use_two_step_variance(x, axis=axis, keepdim=keepdim):
return var_mean_sum_(
x, axis=axis, correction=correction, keepdim=keepdim, return_mean=True
)
return var_mean_welford_(
x, axis=axis, correction=correction, keepdim=keepdim, return_mean=True
)
def pow_recursive(x, y, dtype):
if y < 0:
return pow_recursive(ops.reciprocal(x), -y, dtype)
if y == 0:
return ops.constant(1, dtype)
if y == 1:
return x
result = pow_recursive(x, y // 2, dtype)
result = ops.mul(result, result)
if (y % 2) == 1:
result = ops.mul(result, x)
return result
@make_pointwise
def pow_native(a, b):
return ops.pow(a, b)
fallback_pow = fallback_handler(aten.pow)
@register_lowering(aten.pow, broadcast=True)
def pow(a, b):
if isinstance(b, float) and b == int(b):
return pow(a, int(b))
elif isinstance(b, float) and b == 0.5:
return sqrt(a)
elif isinstance(b, int) and b == 1:
return clone(a)
# Type promotion ensures all tensor arguments have the same type
dtype = next(x.get_dtype() for x in (a, b) if isinstance(x, ir.TensorBox))
is_integer_pow = is_integer_dtype(dtype)
# Optimize away small fixed powers, or for integers avoid falling back to ATen
embed_exponent = isinstance(b, int) and (
-32 < b < 32 or (is_integer_pow and b >= 0)
)
if embed_exponent:
loader = a.make_loader()
def fn(idx):
return pow_recursive(loader(idx), b, a.get_dtype())
return Pointwise.create(
device=a.get_device(),
dtype=a.get_dtype(),
inner_fn=fn,
ranges=a.get_size(),
)
if isinstance(a, Number):
if a == 1:
return full_like(b, 1)
if a == 2 and is_float_dtype(b.get_dtype()):
return exp2(b)
if is_integer_pow:
# ops.pow doesn't work for integers
return fallback_pow(a, b)
return pow_native(a, b)
def mutate_to(changed, val):
if isinstance(changed, TensorBox):
changed_data = changed.data
else:
changed_data = changed
if isinstance(val, TensorBox):
val = val.data
if not isinstance(val, ir.StorageBox):
# introduce a copy to handle views
val = Pointwise.create(
device=changed.get_device(),
dtype=changed.get_dtype(),
inner_fn=val.make_loader(),
ranges=changed.get_size(),
).data
assert isinstance(val, ir.StorageBox)
if isinstance(changed_data, ir.StorageBox) and not (
changed_data.is_input_buffer() or isinstance(changed_data.data, ir.NopKernel)
):
# Fast path, just swing the data pointer
val.realize()
changed_data.data = val.data
return changed
ir.MutationLayout.realize_into(val, changed_data)
return changed
@register_lowering(aten.fill_)
def fill_(x, fill_value):
return mutate_to(x, full_like(x, fill_value))
@register_lowering(aten.copy_, type_promotion_kind=None)
def copy_(dst, src, non_blocking=False):
src = to_device(src, dst.get_device())
src = to_dtype(src, dst.get_dtype())
src = expand(src, dst.get_size())
return mutate_to(dst, src)
@make_pointwise
def floordiv(a, b):
return ops.floordiv(a, b)
@make_pointwise
def truncdiv(a, b):
return ops.truncdiv(a, b)
@register_lowering(aten.div, broadcast=True)
def div_mode(a, b, rounding_mode=None):
both_integer = is_integer_type(a) and is_integer_type(b)
both_boolean = is_boolean_type(a) and is_boolean_type(b)
# floordiv and truncdiv need special handling for integer tensors on Triton,
# see the discussion at https://github.com/openai/triton/issues/605
if rounding_mode == "floor":
assert not both_boolean, "floordiv operands can not be boolean at the same time"
return floordiv(a, b) if both_integer else floor(div(a, b))
if rounding_mode == "trunc":
assert not both_boolean, "truncdiv operands can not be boolean at the same time"
return truncdiv(a, b) if both_integer else trunc(div(a, b))
return div(a, b)
@register_lowering([aten.mul], broadcast=True)
def mul(a, b):
both_bool = is_boolean_type(a) and is_boolean_type(b)
if both_bool:
return logical_and(a, b)
else:
fn = ops_wrapper(aten.mul.__name__)
return make_pointwise(fn)(a, b)
# NOTE: prims.div maps to a / b in C, so performs truncation division on
# integer inputs and true division for floating and complex inputs.
@register_lowering([prims.div], broadcast=True)
def div_prim(a, b):
is_integral = is_boolean_type(a) or is_integer_type(a)
if is_integral:
return truncdiv(a, b)
def fn(*args):
return ops.div(*args)
return make_pointwise(fn)(a, b)
div = register_lowering(
[aten.true_divide, aten.div.Tensor],
broadcast=True,
type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)(div_prim)
@register_lowering([aten.fmod, prims.fmod], broadcast=True)
def fmod(a, b):
is_integral = is_boolean_type(a) or is_integer_type(a)
if is_integral:
def fn(a, b):
return ops.mod(a, b)
else:
def fn(a, b):
return ops.fmod(a, b)
return make_pointwise(fn)(a, b)
@register_lowering(aten.rsqrt)
def rsqrt(x):
dtype = x.get_dtype()
if is_integer_dtype(dtype) or is_boolean_dtype(dtype):
x = to_dtype(x, torch.get_default_dtype())
def _rsqrt(x):
return ops.rsqrt(x)
return make_pointwise(_rsqrt)(x)
@register_lowering([aten.sum, prims.sum])
def sum_(x, axis=None, keepdims=False, *, dtype=None):
if (
is_integer_dtype(x.get_dtype()) or is_boolean_dtype(x.get_dtype())
) and dtype is None:
dtype = torch.int64
fn = make_reduction("sum", override_return_dtype=dtype)
return fn(x, axis, keepdims, dtype=dtype)
@register_lowering(aten.prod)
def prod(x, axis=None, keepdims=False, *, dtype=None):
if (
is_integer_dtype(x.get_dtype()) or is_boolean_dtype(x.get_dtype())
) and dtype is None:
dtype = torch.int64
fn = make_reduction("prod", override_return_dtype=dtype)
return fn(x, axis, keepdims, dtype=dtype)
@register_lowering(aten.any)
def reduce_any(x, dim=None, keepdim=False):
x = to_dtype(x, torch.bool)
return make_reduction("any")(x, axis=dim, keepdims=keepdim)
@register_lowering(aten.max)
def reduce_max(x, dim=None, keepdim=False):
if dim is not None:
return (
reduce_amax(x, axis=dim, keepdims=keepdim),
reduce_argmax(x, axis=dim, keepdims=keepdim),
)
return reduce_amax(x, axis=None, keepdims=keepdim)
@register_lowering(aten.min)
def reduce_min(x, dim=None, keepdim=False):
if dim is not None:
return (
reduce_amin(x, axis=dim, keepdims=keepdim),
reduce_argmin(x, axis=dim, keepdims=keepdim),
)
return reduce_amin(x, axis=None, keepdims=keepdim)
register_lowering(prims.xor_sum)(make_reduction("xor_sum"))
reduce_amax = register_lowering(aten.amax)(make_reduction("max"))
reduce_amin = register_lowering(aten.amin)(make_reduction("min"))
reduce_argmax = register_lowering(aten.argmax)(
make_reduction("argmax", override_return_dtype=torch.int64)
)
reduce_argmin = register_lowering(aten.argmin)(
make_reduction("argmin", override_return_dtype=torch.int64)
)
add = register_pointwise(
aten.add, allow_alpha=True, override_fn_when_input_bool="logical_or"
)
def register_pointwise_numeric(op):
return register_pointwise(
op, type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
)
def register_pointwise_numeric_ldf64(op):
return register_pointwise(
op,
type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
use_libdevice_for_f64=True,
)
exp = register_pointwise_numeric_ldf64(aten.exp)
exp2 = register_pointwise_numeric(aten.exp2)
expm1 = register_pointwise_numeric(aten.expm1)
relu = register_pointwise(aten.relu)
sigmoid = register_pointwise_numeric_ldf64(aten.sigmoid)
sqrt = register_pointwise_numeric_ldf64(aten.sqrt)
square = register_pointwise(aten.square)
sub = register_pointwise(aten.sub, allow_alpha=True)
register_pointwise_numeric_ldf64(aten.cos)
register_pointwise_numeric_ldf64(aten.sin)
register_pointwise(aten.abs)
bitwise_and = register_pointwise(aten.bitwise_and)
bitwise_left_shift = register_pointwise(aten.bitwise_left_shift)
bitwise_not = register_pointwise(
aten.bitwise_not, override_fn_when_input_bool="logical_not"
)
bitwise_or = register_pointwise(aten.bitwise_or)
bitwise_right_shift = register_pointwise(aten.bitwise_right_shift)
bitwise_xor = register_pointwise(aten.bitwise_xor)
register_pointwise_numeric(aten.lgamma)
erf = register_pointwise_numeric(aten.erf)
register_lowering(
aten.special_erf, type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT
)(erf)
register_pointwise_numeric(aten.log1p)
register_pointwise_numeric(aten.tan)
register_pointwise_numeric(aten.tanh)
register_pointwise_numeric_ldf64(aten.log)
logical_and = register_pointwise(
aten.logical_and,
type_promotion_kind=None,
convert_input_to_bool=True,
override_return_dtype=torch.bool,
)
logical_not = register_pointwise(
aten.logical_not,
type_promotion_kind=None,
convert_input_to_bool=True,
override_return_dtype=torch.bool,
)
logical_or = register_pointwise(
aten.logical_or,
type_promotion_kind=None,
convert_input_to_bool=True,
override_return_dtype=torch.bool,
)
logical_xor = register_pointwise(
aten.logical_xor,
type_promotion_kind=None,
convert_input_to_bool=True,
override_return_dtype=torch.bool,
)
maximum = register_pointwise(aten.maximum)
minimum = register_pointwise(aten.minimum)
register_lowering(aten.clamp_min)(maximum)
register_lowering(aten.clamp_max)(minimum)
neg = register_pointwise(aten.neg)
reciprocal = register_pointwise_numeric(aten.reciprocal)
register_pointwise(aten.remainder)
sign = register_pointwise(aten.sign, override_fn_when_input_bool="identity")
register_pointwise(aten.ceil)
register_pointwise(aten.signbit, override_return_dtype=torch.bool)
register_pointwise(aten.le, override_return_dtype=torch.bool)
register_pointwise(aten.lt, override_return_dtype=torch.bool)
register_pointwise(aten.ge, override_return_dtype=torch.bool)
gt = register_pointwise(aten.gt, override_return_dtype=torch.bool)
register_pointwise(aten.eq, override_return_dtype=torch.bool)
register_pointwise(aten.ne, override_return_dtype=torch.bool)
register_pointwise_numeric(aten.cosh)
register_pointwise_numeric(aten.sinh)
register_pointwise_numeric(aten.acos)
register_pointwise_numeric(aten.acosh)
register_pointwise_numeric(aten.asin)
register_pointwise_numeric(aten.asinh)
register_pointwise_numeric(aten.atan2)
register_pointwise_numeric(aten.atan)
register_pointwise_numeric(aten.atanh)
register_pointwise_numeric(aten.copysign)
register_pointwise_numeric(aten.erfc)
register_pointwise_numeric(aten.erfinv)
register_pointwise_numeric(aten.hypot)
register_pointwise_numeric(aten.log10)
register_pointwise_numeric(aten.nextafter)
register_foreach_pointwise(aten._foreach_add.List, add, allow_alpha=True)
register_foreach_pointwise(aten._foreach_add.Scalar, add, allow_alpha=True)
register_foreach_pointwise(aten._foreach_mul.List, mul)
register_foreach_pointwise(aten._foreach_mul.Scalar, mul)
register_foreach_pointwise(aten._foreach_sub.List, sub)
register_foreach_pointwise(aten._foreach_sub.Scalar, sub)
register_foreach_pointwise(aten._foreach_neg.default, neg)
register_foreach_pointwise(aten._foreach_pow.Scalar, pow)
register_foreach_pointwise(aten._foreach_pow.ScalarAndTensor, pow)
register_foreach_pointwise(aten._foreach_div.List, div)
register_foreach_pointwise(aten._foreach_div.Scalar, div)
register_foreach_pointwise(aten._foreach_sqrt, sqrt)
register_foreach_pointwise(aten._foreach_maximum.List, maximum)
register_foreach_pointwise(aten._foreach_maximum.Scalar, maximum)
register_foreach_pointwise(aten._foreach_reciprocal, reciprocal)
register_foreach_pointwise(aten._foreach_sign, sign)
register_foreach_pointwise(aten._foreach_copy, copy)
def register_inplace(aten_op, outplace_op):
@register_lowering(aten_op, type_promotion_kind=None)
def fn(*args, **kwargs):
result = outplace_op(*args, **kwargs)
result = to_dtype(result, args[0].get_dtype())
return mutate_to(args[0], result)
return fn
register_inplace(aten.add_, add)
register_inplace(aten.bitwise_and_, bitwise_and)
register_inplace(aten.bitwise_left_shift_, bitwise_left_shift)
register_inplace(aten.bitwise_not_, bitwise_not)
register_inplace(aten.bitwise_or_, bitwise_or)
register_inplace(aten.bitwise_right_shift_, bitwise_right_shift)
register_inplace(aten.bitwise_xor_, bitwise_xor)
register_inplace(aten.mul_, mul)
register_inplace(aten.div_.Tensor, div)
register_inplace(aten.div_.Tensor_mode, div_mode)
register_inplace(aten.logical_and_, logical_and)
register_inplace(aten.logical_not_, logical_not)
register_inplace(aten.logical_or_, logical_or)
register_inplace(aten.logical_xor_, logical_xor)
register_inplace(aten.sub_, sub)
register_inplace(aten.relu_, relu)
register_inplace(aten.sigmoid_, sigmoid)
register_lowering(aten.__and__)(bitwise_and)
register_lowering(aten.__lshift__)(bitwise_left_shift)
register_lowering(aten.__or__)(bitwise_or)
register_lowering(aten.__rshift__)(bitwise_right_shift)
register_lowering(aten.__xor__)(bitwise_xor)
register_inplace(aten.__iand__, aten.__and__)
register_inplace(aten.__ilshift__, aten.__lshift__)
register_inplace(aten.__ior__, aten.__or__)
register_inplace(aten.__irshift__, aten.__rshift__)
register_inplace(aten.__ixor__, aten.__xor__)
@register_lowering(aten.sym_constrain_range)
def sym_constrain_range(a, min, max):
tracing_context = torch._guards.TracingContext.get()
assert tracing_context is not None
assert a in tracing_context.fake_mode.shape_env.var_to_range
return a
@register_lowering(aten.sym_size)
def sym_size(a, dim):
return a.get_size()[dim]
@register_lowering(aten.sym_stride)
def sym_stride(a, dim):
return a.get_stride()[dim]
@register_lowering(aten.sym_numel)
def sym_numel(a):
return a.get_numel()
for method, func in magic_methods.items():
register_lowering(method_to_operator(method))(func)
@register_lowering(aten._foobar)
def foobar(self, *args, **kwargs):
raise NotImplementedError("Helpful for debugging")
@register_lowering(torch.ops._inductor_test.realize)
def _realize(x):
x.realize()
return clone(x)
try:
import torch.distributed._functional_collectives
c10d_functional = torch.ops.c10d_functional
@register_lowering(c10d_functional.wait_tensor)
def wait(input):
return TensorBox.create(ir.Wait.create(input))
@register_lowering(c10d_functional.all_reduce)
def allreduce(input, reduce_op, tag, ranks, group_size):
return ir.AllReduce.create(input, reduce_op, tag, ranks, group_size)
@register_lowering(c10d_functional.all_gather_into_tensor)
def all_gather_into_tensor(shard, tag, ranks, group_size):
return TensorBox.create(
ir.AllGatherIntoTensor.create(shard, tag, ranks, group_size)
)
@register_lowering(c10d_functional.reduce_scatter_tensor)
def reduce_scatter_tensor(input, reduce_op, tag, ranks, group_size):
return TensorBox.create(
ir.ReduceScatterTensor.create(input, reduce_op, tag, ranks, group_size)
)
@register_lowering(c10d_functional.all_reduce_coalesced)
def all_reduce_coalesced(input, reduce_op, tag, ranks, group_size):
return ir.AllReduceCoalesced.create(input, reduce_op, tag, ranks, group_size)
@register_lowering(c10d_functional.all_gather_into_tensor_coalesced)
def all_gather_into_tensor_coalesced(self, tag, ranks, group_size):
result = ir.AllGatherIntoTensorCoalesced.create(self, tag, ranks, group_size)
return list(map(TensorBox.create, result))
@register_lowering(c10d_functional.reduce_scatter_tensor_coalesced)
def reduce_scatter_tensor_coalesced(self, reduceOp, tag, ranks, group_size):
result = ir.ReduceScatterTensorCoalesced.create(
self, reduceOp, tag, ranks, group_size
)
return list(map(TensorBox.create, result))
except ImportError:
log.info(
"Inductor support for distributed collectives depends on building torch.distributed"
)
# populate lowerings defined in kernel/*
from . import kernel
import_submodule(kernel)
from . import quantized_lowerings
quantized_lowerings.register_quantized_ops()