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android / platform / external / pytorch / 02196c21ac / . / torch / _inductor / ir.py
blob: 84e190e0280fda19ca6530add273a9f91b397543 [file] [log] [blame]
import collections
import contextlib
import dataclasses
import functools
import itertools
import logging
import re
import textwrap
import traceback
from contextlib import nullcontext
from enum import Enum
from functools import partial
from inspect import signature
from typing import (
Any,
Callable,
ClassVar,
Dict,
Iterable,
List,
Optional,
Sequence,
Set,
Tuple,
Union,
)
from unittest.mock import patch
import sympy
from sympy import Expr, Integer
import torch._export.serde.schema as export_schema
import torch._logging
import torch.fx
import torch.utils._pytree as pytree
from torch._dynamo.device_interface import get_interface_for_device
from torch._dynamo.utils import identity
from torch._export.serde.serialize import GraphModuleSerializer
from torch._prims_common import (
compute_required_storage_length,
is_boolean_dtype,
is_float_dtype,
make_channels_last_strides_for,
make_contiguous_strides_for,
StrideType,
)
from torch._subclasses.fake_tensor import get_schema_info
from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols, SymTypes
from torch.utils._sympy.functions import CleanDiv, FloorDiv, ModularIndexing
from . import config, dependencies
from .codegen.common import index_prevent_reordering
from .dependencies import (
extract_input_node_reduction_ranges,
extract_read_writes,
var_builder,
)
from .utils import (
argsort,
cache_on_self,
convert_shape_to_inductor,
convert_shape_to_symint,
developer_warning,
get_kernel_metadata,
is_dynamic,
pad_listlike,
sympy_dot,
sympy_product,
sympy_subs,
sympy_symbol,
)
from .virtualized import ops, V
log = logging.getLogger(__name__)
indent = functools.partial(textwrap.indent, prefix=" ")
aten = torch.ops.aten
""" [Note: Inductor IR]
Inductor's IR is produced by executing 'lowering' code (see lowering.py). Each
lowering is registered to a particular aten operator, and expects inputs that
correspond to the aten schema. However, in place of torch Tensor inputs, lowerings
expect Inductor TensorBox inputs.
TensorBox IR represents torch tensors. Tensors are sometimes single objects owning
storage, and sometimes views of another Tensor's storage. Mutating tensor operations
(such as add_()) affect the underlying storage and any associated views. Other operations
(such as .t_()) update metadata about the current view but don't modify the underlying storage.
To model this in Inductor, the IR distinguishes between TensorBox, View, StorageBox and Buffer.
TensorBox is the top level IR construct that any lowering should produce and maps to a torch.Tensor
output from an operation. But just as torch.Tensors take different forms, TensorBox IR can
reference View IR or directly reference StorageBox IRs.
Some Inductor lowerings produce new sets of 'Box'es, while others (such as .t() or other view ops)
may take an existing TensorBox and point it to a new underlying View IR.
Tensors that directly own storage are represented as a chain of:
TensorBox -> StorageBox -> Buffer
where Buffer is a simple (1D) allocation, and StorageBox introduces the concept of a Layout.
If you mutate the data of such a tensor, we swing the StorageBox pointer to point to a new buffer
(leaving the old buffer unmodified and functionalizing the operation).
Tensors backed by views add one more indirection to the IR.
TensorBox -> View -> StorageBox -> Buffer
In these cases, the underlying StorageBox/Buffer will be shared with the pre-view TensorBox.
"""
def validate_ir(node_or_nodes):
def _check_tensorbox(nodes):
# Could expand this to check deeper properties
# (e.g. TensorBox points to View or StorageBox)
if isinstance(nodes, (list, tuple)):
for node in nodes:
_check_tensorbox(node)
elif isinstance(nodes, dict):
for node in nodes.values():
_check_tensorbox(node)
else:
assert isinstance(
nodes,
(
torch._inductor.ir.ExpandView,
DynamicScalar,
TensorBox,
sympy.Symbol,
sympy.logic.boolalg.Boolean,
Expr,
),
), f"Found {type(nodes)}, which is not a supported top level IR node. See [Note: Inductor IR]"
# Be picky about the accepted data structure (don't use pytree here)
_check_tensorbox(node_or_nodes)
def ops_wrapper(name):
assert isinstance(name, str)
def fn(*args, **kwargs):
return getattr(ops, name)(*args, **kwargs)
return fn
def inverse_reorder(order):
inv_order = dict(zip(order, range(len(order))))
def reindex(index):
assert len(index) == len(inv_order)
return [index[inv_order[i]] for i in range(len(index))]
return reindex
def same_reorder(order):
def reindex(index):
assert len(index) == len(order)
return [index[order[i]] for i in range(len(index))]
return reindex
def fuse_reindexing(reindex1, reindex2):
def reindex(index):
return reindex1(reindex2(index))
return reindex
NHWC_STRIDE_ORDER = [3, 0, 2, 1]
def stride_order2fill_order(order):
"""
Convert stride order to fill order
For channel last format,
stride order = [3, 0, 2, 1] and fill order = [1, 3, 2, 0]
"""
lookup = {pos: idx for idx, pos in enumerate(order)}
fill_order = [lookup[i] for i in range(len(order))]
return fill_order
def get_stride_order(seq: Sequence[int]) -> List[int]:
"""
Convert strides to stride order
"""
sorted_idx: List[int] = argsort(seq)
out = [0 for _ in range(len(seq))]
for i, elem in enumerate(sorted_idx):
out[elem] = i
return out
def ir_node_to_tensor(x, guard_shape=True):
if x is None:
return None
shape_fn: Callable[[Expr], Union[int, Expr]]
if not guard_shape:
shape_fn = V.graph.sizevars.size_hint
else:
shape_fn = identity
size = [shape_fn(s) for s in x.get_size()]
stride: StrideType
if is_storage_and_layout(x):
stride = [shape_fn(s) for s in x.get_layout().stride]
else:
stride = make_contiguous_strides_for(size)
dtype = x.get_dtype()
device = x.get_device()
size = convert_shape_to_symint(size)
stride = convert_shape_to_symint(stride)
t = torch.empty_strided(
size=size, stride=stride, dtype=dtype, device=device
).zero_()
return t
def may_convert_to_optional(value):
if isinstance(value, list) and not value:
# [None] makes sure the cpp wrapper codegen will generate something like
# {c10::nullopt} instead of {}
return [None]
return value
def get_device_type(x):
if getattr(x, "get_device", None):
return get_device_type(x.get_device())
if isinstance(x, torch.device):
return x.type
return None
def is_triton(x):
return get_device_type(x) == "cuda"
def is_cpu(x):
return get_device_type(x) == "cpu"
class IRNode:
_current_origins: ClassVar[Set[Any]] = set()
@staticmethod
@contextlib.contextmanager
def current_origins(origins: Set[torch.fx.Node]):
old = IRNode._current_origins
IRNode._current_origins = old | origins
try:
yield
finally:
IRNode._current_origins = old
def __post_init__(self):
self.origins = set(self._current_origins)
self.traceback = traceback.format_stack() if config.debug_ir_traceback else None
def get_traceback(self):
return self.traceback
def common_repr(self):
origins = f"origins={getattr(self, 'origins', '')}"
if len(origins) > 64:
# this can get *very* long
origins = f"{origins[:61]}..."
return [origins]
def str_helper(self, lines):
lines = lines + self.common_repr()
lines = indent(",\n".join(map(str, lines)))
return f"{type(self).__name__}(\n{lines}\n)"
def is_user_of(self, name):
return name in self.get_read_names()
@cache_on_self
def get_read_names(self):
return {dep.name for dep in self.get_reads()}
def get_layout(self):
raise NotImplementedError(f"get_layout() is not implemented by {type(self)}!")
def get_size(self):
raise NotImplementedError(f"get_size() is not implemented by {type(self)}!")
def get_numel(self):
return sympy_product(self.get_size())
def is_zero_elements(self):
return V.graph.sizevars.is_expr_static_and_true(sympy.Eq(self.get_numel(), 0))
def realize(self):
"""
If the IRNode refers to data which has not been materialized (e.g.,
it is a Pointwise/Reduction that could potentially have more
compute fused into it), realize the IRNode into physical memory,
ending the possibility of fusing into it, but allowing, e.g., multiple
users to access the data without having to recompute.
Check StorageBox.realize for a particularly notable implementation.
TODO(ezyang): I think, in principle, every IRNode should have an
implementation of this, and most of the time no-op is OK, but you
really do have to audit each IRNode for this, so for now, raise
an error if it's not implemented. Note that some code in graph.py
will catch this thrown error and suppress it with a warning.
"""
raise NotImplementedError(f"realize NYI on {type(self)}")
def codegen_reference(self, writer=None):
raise NotImplementedError(f"codegen_reference NYI on {type(self)}")
# The abstract method declarations below serve to convince mypy that all IRNode instances have these functions
# defined, while having no effect at runtime. We cannot create stub implementations here because other parts of
# the code dynamically check for defined attributes.
get_device: Callable[[], torch.device]
get_dtype: Callable[[], torch.dtype]
get_name: Callable[[], str]
get_reads: Callable[[], Any]
get_stride: Callable[[], Any]
get_storage_numel: Callable[[], Any]
has_exceeded_max_reads: Callable[[], bool]
make_loader: Callable[[], Callable[[Any], Any]]
make_indexer: Callable[[], Callable[[Any], Any]]
mark_reuse: Callable[[int], None]
realize_hint: Callable[[], None]
@dataclasses.dataclass
class Loops(IRNode):
device: torch.device
dtype: torch.dtype
inner_fn: Callable[..., Any]
ranges: List[Expr]
def __str__(self, names=("ranges",)):
return self.str_helper(
[
f"'{self.device.type}'",
str(self.dtype),
self.inner_fn_str(),
]
+ [f"{name}={getattr(self, name)}" for name in names]
+ [f"origin_node={self.origin_node!r}"]
)
def __post_init__(self):
super().__post_init__()
self.origin_node = None
__repr__ = __str__
def get_dtype(self):
return self.dtype
def get_device(self):
return self.device
def get_origin_node(self):
return self.origin_node
def get_size(self):
return self.ranges
def get_pointwise_size(self):
return self.ranges
def is_extern(self):
return False
@classmethod
def create(cls, *args, **kwargs):
origin_node = kwargs.pop("origin_node", None)
tb = kwargs.pop("traceback", None)
r = cls(*args, **kwargs)
r.origin_node = origin_node
r.traceback = (
tb or traceback.format_stack() if config.debug_ir_traceback else None
)
return TensorBox.create(r)
@staticmethod
def _index(ranges, prefix="i"):
return [
sympy.Integer(0) if s == 1 else sympy_symbol(f"{prefix}{n}")
for n, s in enumerate(ranges)
]
@cache_on_self
def inner_fn_str_len(self):
return len(self.inner_fn_str())
def inner_fn_str(self):
index = self._index(self.ranges)
return V.KernelFormatterHandler.ir_to_string(self.inner_fn, index)
def get_reads(self):
with patch.object(FlexibleLayout, "allow_indexing", True):
if self.get_reduction_type():
return extract_read_writes(
self.make_loader(),
self.get_size(),
self.get_reduction_size(),
).reads
else:
return extract_read_writes(
self.make_loader(),
self.get_size(),
).reads
def get_reduction_size(self):
raise NotImplementedError(
f"get_reduction_size() is not implemented by {type(self)}!"
)
def get_reduction_type(self):
raise NotImplementedError(
f"get_reduction_type() is not implemented by {type(self)}!"
)
def constant_to_device(self, device):
raise NotImplementedError(
f"constant_to_device() is not implemented by {type(self)}!"
)
def nop_loader_fn(idx, *, dtype):
if dtype.is_floating_point:
return ops.constant(float("nan"), dtype)
else:
return ops.constant(0, dtype)
class Pointwise(Loops):
def make_loader(self):
# Make zero-element loops into a no-op
if self.is_zero_elements():
return partial(nop_loader_fn, dtype=self.dtype)
return self.inner_fn
def get_reduction_size(self):
return []
def get_reduction_type(self):
return None
def store_output(self, output_name, indexer, vars):
loader = self.make_loader()
return ops.store(output_name, indexer(vars), loader(vars))
def constant_to_device(self, device):
"""Move this to a given device. Requires that all reads are to constants."""
loader = self.make_loader()
loader = patch.object(ConstantBuffer, "override_device", device)(loader)
return Pointwise(device, self.dtype, loader, self.ranges)
@dataclasses.dataclass
class Scatter(Pointwise):
output_indexer: Callable[[List[Expr]], Expr]
scatter_mode: Optional[str] = None
def constant_to_device(self, device):
"""Move this to a given device. Requires that all reads are to constants."""
loader = self.make_loader()
loader = patch.object(ConstantBuffer, "override_device", device)(loader)
return Scatter(
device,
self.dtype,
loader,
self.ranges,
self.output_indexer,
self.scatter_mode,
)
def store_output(self, output_name, indexer, vars):
loader = self.make_loader()
return ops.store(
output_name,
indexer(self.output_indexer(vars)),
loader(vars),
mode=self.scatter_mode,
)
class ReductionHint(Enum):
INNER = 0
OUTER = 1
OUTER_TINY = 2
DEFAULT = 3
class TileHint(Enum):
SQUARE = 0
DEFAULT = 1
REDUCTION_COMBINE_FN = {
"any": ops_wrapper("logical_or"),
"max": ops_wrapper("maximum"),
"min": ops_wrapper("minimum"),
"prod": ops_wrapper("mul"),
"sum": ops_wrapper("add"),
"xor_sum": ops_wrapper("bitwise_xor"),
}
def get_reduction_combine_fn(reduction_type, dtype):
if reduction_type in REDUCTION_COMBINE_FN:
combine_fn = REDUCTION_COMBINE_FN[reduction_type]
elif reduction_type in {"argmax", "argmin"}:
def combine_fn(a, b):
a_value, a_index = a
b_value, b_index = b
if reduction_type == "argmin":
mask = ops.lt(a_value, b_value)
else:
mask = ops.gt(a_value, b_value)
equal = ops.eq(a_value, b_value)
if is_float_dtype(dtype):
a_isnan = ops.ne(a_value, a_value)
b_isnan = ops.ne(b_value, b_value)
mask = ops.logical_or(mask, ops.gt(a_isnan, b_isnan))
equal = ops.logical_or(equal, ops.logical_and(a_isnan, b_isnan))
mask = ops.logical_or(
mask, ops.logical_and(equal, ops.lt(a_index, b_index))
)
return (
ops.where(mask, a_value, b_value),
ops.where(mask, a_index, b_index),
)
elif reduction_type == "welford_combine":
def combine_fn(a, b):
a_mean, a_m2, a_weight = a
b_mean, b_m2, b_weight = b
delta = b_mean - a_mean
new_weight = a_weight + b_weight
w2_over_w = b_weight / new_weight
return (
a_mean + delta * w2_over_w,
a_m2 + b_m2 + delta * delta * a_weight * w2_over_w,
new_weight,
)
else:
raise NotImplementedError(f"unknown reduction_type={reduction_type}")
return combine_fn
@dataclasses.dataclass
class Reduction(Loops):
reduction_ranges: List[Expr]
reduction_type: str
# self.dtype represents the dst dtype
src_dtype: torch.dtype
reduction_hint: ReductionHint
def __str__(self):
return Loops.__str__( # type: ignore[call-arg]
self, names=("ranges", "reduction_ranges", "reduction_type")
)
def __repr__(self):
return self.__str__()
def get_reduction_size(self):
return self.reduction_ranges
def get_reduction_type(self):
return self.reduction_type
def store_reduction(self, output_name, indexer, vars, reduction_vars):
value = ops.reduction(
self.dtype,
self.src_dtype,
self.reduction_type,
self.inner_fn(vars, reduction_vars),
)
return ops.store_reduction(output_name, indexer(vars), value)
def index_length(self):
return len(self.ranges) + len(self.reduction_ranges)
def inner_fn_str(self):
index = self._index(self.ranges)
rindex = self._index(self.reduction_ranges, "r")
return V.KernelFormatterHandler.ir_to_string(
self.inner_fn,
index,
rindex,
)
def constant_to_device(self, device):
"""Move this to a given device. Requires that all reads are to constants."""
loader = self.make_loader()
loader = patch.object(ConstantBuffer, "override_device", device)(loader)
return Reduction(
device,
self.dtype,
loader,
self.ranges,
self.reduction_ranges,
self.reduction_type,
self.src_dtype,
ReductionHint.DEFAULT,
)
@staticmethod
def num_splits(
device,
dst_dtype,
src_dtype,
inner_fn,
ranges,
reduction_ranges,
reduction_type,
reduction_numel,
input_node: Optional[IRNode] = None,
):
def _is_static(x):
return isinstance(x, (int, sympy.Integer))
reduction_numel_hint = V.graph.sizevars.symbolic_hint(reduction_numel)
numel_hint = V.graph.sizevars.symbolic_hint(sympy_product(ranges))
should_split = (
is_triton(device)
and reduction_type
not in {
"argmax",
"argmin",
}
and config.split_reductions
# We don't support unbacked symints
and _is_static(reduction_numel_hint)
and _is_static(numel_hint)
)
if not should_split:
return ReductionHint.DEFAULT, 1
device_interface = get_interface_for_device(get_device_type(device))
num_sm = device_interface.Worker.get_device_properties(
device
).multi_processor_count
min_elements_per_thread = 32
max_elements_per_thread = 512
threads_per_sm = 2048
min_elements_per_device = min_elements_per_thread * num_sm * threads_per_sm
max_elements_per_device = max_elements_per_thread * num_sm * threads_per_sm
def inner_reduction_splits(reduction_numel_hint, numel_hint):
# do heuristics that's close to eager mode for split inner reduction
# we leak reduction autotune configs here, and will need to refactor to avoid this later
num_warps = 8
num_threads = 32 * num_warps
if numel_hint >= 2 * num_sm: # don't split if there are enough outputs
return 1
if reduction_numel_hint <= 8192:
return 1
if reduction_numel_hint * numel_hint <= min_elements_per_device:
split_size = min_elements_per_thread
elif reduction_numel_hint * numel_hint < max_elements_per_device:
target_blocks = num_sm * threads_per_sm // (2 * num_threads)
blocks_per_output = (target_blocks + numel_hint - 1) // numel_hint
tmp_split_size = (
reduction_numel_hint + num_threads * blocks_per_output - 1
) // (num_threads * blocks_per_output)
divisors = sympy.divisors(reduction_numel_hint)
closest = min(divisors, key=lambda x: abs(x - tmp_split_size))
if abs(closest - tmp_split_size) < 30:
# prefer even splits, but never smalle than min_elements_per_thread
split_size = max(closest, min_elements_per_thread)
else:
split_size = tmp_split_size
else:
divisors = sympy.divisors(reduction_numel_hint)
closest = min(divisors, key=lambda x: abs(x - max_elements_per_thread))
if abs(closest - max_elements_per_thread) < 50:
# prefer even splits
split_size = closest
else:
split_size = max_elements_per_thread
return (reduction_numel_hint + split_size * num_threads - 1) // (
split_size * num_threads
)
def outer_reduction_splits(reduction_numel_hint, numel_hint):
# TODO the best heuristic currently has XBLOCK (corresponding to numel_hint) 128
# extend to even smaller number of outputs
num_warps = 8
num_threads = num_warps * 32
rvals_per_thread = 4 # comes from heuristics, refactor to not leak here
xvals_per_block = 128
xblocks = (numel_hint + xvals_per_block - 1) // xvals_per_block
if reduction_numel_hint * numel_hint < min_elements_per_device:
split_size = min_elements_per_thread
elif reduction_numel_hint * numel_hint < max_elements_per_device:
target_blocks = num_sm * threads_per_sm // (num_threads)
target_blocks = (target_blocks + xblocks - 1) // xblocks
tmp_split_size = (
reduction_numel_hint + rvals_per_thread * target_blocks - 1
) // (rvals_per_thread * target_blocks)
divisors = sympy.divisors(reduction_numel_hint)
closest = min(divisors, key=lambda x: abs(x - tmp_split_size))
if abs(tmp_split_size - closest) < 20:
split_size = max(closest, min_elements_per_thread)
else:
split_size = tmp_split_size
else:
divisors = sympy.divisors(reduction_numel_hint)
closest = min(divisors, key=lambda x: abs(x - max_elements_per_thread))
if abs(closest - max_elements_per_thread) < 50:
# prefer even splits
split_size = closest
else:
split_size = max_elements_per_thread
return (reduction_numel_hint + rvals_per_thread * split_size - 1) // (
rvals_per_thread * split_size
)
# easy cases
if numel_hint == 1:
split = inner_reduction_splits(reduction_numel_hint, numel_hint)
if split == 1:
# No need to split.
return ReductionHint.INNER, split
if (
len(ranges) == 0
and input_node is not None
and isinstance(input_node, TensorBox)
):
# Only handles the case where keep_dim = False.
# Otherwise, we need to propagate reduction dim info to the stage where
# the intermediate loader of the first Reduction is generated.
new_ranges, new_reduction_ranges = extract_input_node_reduction_ranges(
input_node
)
if new_ranges is not None and new_reduction_ranges is not None:
extracted_numel_hint = V.graph.sizevars.symbolic_hint(
sympy_product(new_ranges + new_reduction_ranges)
)
if reduction_numel_hint == extracted_numel_hint:
log.debug(
"Use previous IRNode's range and reduction_ranges instead of split. "
"current ranges: %s, current reduction ranges: %s, current split: %d, "
"new ranges: %s, new reduction ranges: %s",
ranges,
reduction_ranges,
split,
new_ranges,
new_reduction_ranges,
)
# If the input_node or its dependent nodes are also Reduction nodes,
# use reduction_sizes of this node or its dependent nodes directly.
return ReductionHint.INNER, -1
return ReductionHint.INNER, split
if (
reduction_numel_hint <= min_elements_per_thread
or numel_hint >= num_sm * 2 * 32
):
return ReductionHint.DEFAULT, 1
r = Reduction(
device,
dst_dtype,
inner_fn,
ranges,
reduction_ranges,
reduction_type,
src_dtype,
ReductionHint.DEFAULT,
)
def get_read_indices(r):
cb = ComputedBuffer(
name=None,
layout=FlexibleLayout(
device=r.get_device(),
dtype=r.get_dtype(),
size=r.get_size(),
),
data=r,
)
read_writes = cb.get_read_writes()
# try finding the full size producer
# TODO this will fail for something like ((1, N) * (N, 1)).sum()
# this would also possibly be wrong for producers with the different contiguity but we hope those cases are rare
range_vars = [
r
for r in read_writes.range_vars
if isinstance(r, sympy.Expr) and not isinstance(r, sympy.Number)
]
indices = []
changed = False
for md in sorted(read_writes.reads, key=lambda x: x.name):
if all(r in md.index.free_symbols for r in range_vars):
indices.append(md.index)
if md.name in V.graph.name_to_buffer:
buf = V.graph.name_to_buffer[md.name]
original_stride = buf.layout.stride
buf.decide_layout()
if buf.layout.stride != original_stride:
changed = True
return indices, changed
indices, changed = get_read_indices(r)
if changed:
indices, _ = get_read_indices(r)
if len(indices) == 0:
# TODO determine splits when all inputs are broadcast
return ReductionHint.DEFAULT, 1
(_, reduction_vars), ranges = dependencies.index_vars_squeeze(
r.get_size(), r.get_reduction_size()
)
num_outer = 0
num_inner = 0
for i in indices:
i = V.graph.sizevars.simplify_with_ranges(i, ranges)
strides = V.graph.sizevars.stride_hints(i, reduction_vars, ranges.keys())
outer = all(s > 1 for s in strides)
if outer:
num_outer += 1
else:
num_inner += 1
if num_inner > num_outer:
return ReductionHint.INNER, inner_reduction_splits(
reduction_numel_hint, numel_hint
)
else:
return ReductionHint.OUTER, outer_reduction_splits(
reduction_numel_hint, numel_hint
)
@staticmethod
def _unroll_reduction_fn(inner_fn, reduction_ranges, reduction_type, src_dtype):
"""Convert inner_fn from a reduction to an pointwise"""
reduction_ranges = [
V.graph.sizevars.evaluate_static_shape(x) for x in reduction_ranges
]
combine_fn = get_reduction_combine_fn(reduction_type, src_dtype)
def fn(index):
return functools.reduce(
combine_fn,
(
value_fn(index, rindex)
for rindex in itertools.product(
*[range(x) for x in reduction_ranges]
)
),
)
if reduction_type in ("argmin", "argmax"):
flatten_index = FixedLayout(
None, # type: ignore[arg-type]
None, # type: ignore[arg-type]
reduction_ranges,
FlexibleLayout.contiguous_strides(reduction_ranges),
).make_indexer()
def value_fn(index, rindex):
rindex = [sympy.expand(i) for i in rindex]
return (
inner_fn(index, rindex),
ops.index_expr(flatten_index(rindex), torch.int64),
)
return lambda index: fn(index)[1]
else:
value_fn = inner_fn
return fn
@classmethod
def create( # type: ignore[override]
cls,
device: torch.device,
dst_dtype: torch.dtype,
src_dtype: torch.dtype,
inner_fn: Callable[..., Any],
ranges: List[Expr],
reduction_ranges: List[Expr],
reduction_type: str,
reduction_hint: ReductionHint = ReductionHint.DEFAULT,
input_node: Optional[IRNode] = None,
):
reduction_numel = V.graph.sizevars.simplify(sympy_product(reduction_ranges))
if reduction_numel == 0:
# N.B. This is a hack to generate the literal of the given type
# Ideally, we should be fixing `def constant` in triton.py
# but it breaks due to hardcoded dtypes in other places
def py_cnst(val):
return (
bool(val)
if dst_dtype == torch.bool
else float(val)
if dst_dtype.is_floating_point
else int(val)
)
rtypes_to_inits = {
"sum": py_cnst(0),
"xor_sum": py_cnst(0),
"prod": py_cnst(1),
"any": py_cnst(0),
# "all" is desugared to `!any(!val)`
}
assert (
reduction_type in rtypes_to_inits.keys()
), f"{reduction_type} not supported for zero-dimension tensors!"
def const_fn(index):
return ops.constant(rtypes_to_inits[reduction_type], dst_dtype)
return Pointwise.create(
device=device,
dtype=src_dtype,
inner_fn=const_fn,
ranges=list(ranges),
)
if reduction_numel == 1:
# this reduction is actually a pointwise op
if reduction_type in ("argmin", "argmax"):
def fn(index):
return ops.constant(0, dst_dtype)
else:
def fn(index):
reduction_index = [sympy.Integer(0) for _ in reduction_ranges]
return inner_fn(index, reduction_index)
return Pointwise.create(device, dst_dtype, fn, ranges)
if (
isinstance(reduction_numel, sympy.Integer)
and V.graph.sizevars.size_hint(reduction_numel)
< config.unroll_reductions_threshold
and sympy_product(ranges) != 1
):
return Pointwise.create(
device,
dst_dtype,
cls._unroll_reduction_fn(
inner_fn, reduction_ranges, reduction_type, src_dtype
),
ranges,
)
# triton doesn't support reduce to single element well, so break it up
hint, split = cls.num_splits(
device,
dst_dtype,
src_dtype,
inner_fn,
ranges,
reduction_ranges,
reduction_type,
reduction_numel,
input_node,
)
# intermediate reduction in split can contain complex indexing,
# and num_splits will fail to correctly set the hint
# reuse the passed hint if available
if reduction_hint == ReductionHint.DEFAULT:
reduction_hint = hint
if split == -1:
assert input_node is not None
new_ranges, new_reduction_ranges = extract_input_node_reduction_ranges(
input_node # type: ignore[arg-type]
)
assert new_ranges is not None
assert new_reduction_ranges is not None
return cls.create_multilayer_existing_ranges(
device,
dst_dtype,
src_dtype,
inner_fn,
ranges,
reduction_ranges,
new_ranges,
new_reduction_ranges,
reduction_type,
reduction_hint,
)
elif split > 1:
# triton doesn't support reduce to single element well, so break it up
return cls.create_multilayer(
device,
dst_dtype,
src_dtype,
inner_fn,
ranges,
reduction_ranges,
reduction_type,
split,
reduction_hint,
)
return TensorBox.create(
Reduction(
device,
dst_dtype,
inner_fn,
ranges,
reduction_ranges,
reduction_type,
src_dtype,
reduction_hint,
)
)
@staticmethod
def default_accumulator(reduction_type, dtype):
if reduction_type in {"max", "argmax"}:
if is_float_dtype(dtype):
return float("-inf")
elif is_boolean_dtype(dtype):
return 0
else:
return torch.iinfo(dtype).min
if reduction_type in {"min", "argmin"}:
if is_float_dtype(dtype):
return float("inf")
elif is_boolean_dtype(dtype):
return 1
else:
return torch.iinfo(dtype).max
return {
"sum": 0,
"prod": 1,
"xor_sum": 0,
"any": 0,
"welford_reduce": (0, 0, 0),
"welford_combine": (0, 0, 0),
}[reduction_type]
@staticmethod
def default_value(reduction_type, dtype):
if reduction_type == "welford_reduce":
return 0
return Reduction.default_accumulator(reduction_type, dtype)
@staticmethod
def _multilayer_second_step_hint(
split: int, numel_hint: int, reduction_hint: ReductionHint
) -> ReductionHint:
if split == -1:
return reduction_hint
if split <= 512 and numel_hint <= 512 and reduction_hint == ReductionHint.OUTER:
return ReductionHint.OUTER_TINY
if (
split <= 1024
and numel_hint <= 256
and reduction_hint == ReductionHint.OUTER
):
return ReductionHint.OUTER_TINY
return reduction_hint
@classmethod
def _multilayer_wrap_loader(
cls,
loader,
reduction_ranges,
reduction_numel,
split,
block_size,
default,
):
reindex = View.dynamic_reshape_indexer(reduction_ranges, [reduction_numel])
need_mask = not V.graph.sizevars.is_expr_static_and_true(
sympy.Eq(reduction_numel % split, 0)
)
def wrapper_fn(index, reduction_index):
(reduction_index,) = reduction_index
*new_index, reduction_block = index
indices = block_size * reduction_block + reduction_index
def body():
return loader(new_index, reindex([indices]))
if need_mask:
mask = ops.lt(
ops.index_expr(indices, torch.int32),
ops.index_expr(reduction_numel, torch.int32),
)
return ops.masked(mask, body, default)
else:
return body()
return wrapper_fn
@classmethod
def _multilayer_wrap_loader_existing_ranges(
cls,
loader,
original_ranges,
original_reduction_ranges,
new_ranges,
new_reduction_ranges,
default,
):
assert len(original_ranges) == 0, f"{original_ranges}= is not equal to []"
reindex = View.dynamic_reshape_indexer(
original_reduction_ranges, tuple(new_ranges) + tuple(new_reduction_ranges)
)
def wrapper_fn(index, reduction_index):
return loader([], reindex(tuple(index) + tuple(reduction_index)))
return wrapper_fn
@classmethod
def create_multilayer_helper(
cls,
device: torch.device,
dst_dtype: torch.dtype,
src_dtype: torch.dtype,
wrapper_fn: Callable[..., Any],
original_ranges: List[Expr],
original_reduction_ranges: List[Expr],
new_ranges: List[Expr],
new_reduction_ranges: List[Expr],
reduction_type: str,
split: int,
reduction_hint: ReductionHint,
):
"""
Break a large reduction up into multiple smaller reductions
recursively
"""
# triton will automatically compute reductions in fp32 if reducing over fp16/bf16
# within the kernel. keep the intermediate in fp32 so as to keep the whole reduction
# in fp32 and not reduce precision by breaking up the kernel into multiple layers
intermediate_dtype = (
dst_dtype
if dst_dtype not in (torch.float16, torch.bfloat16)
else torch.float
)
intermediate = Reduction.create(
device,
intermediate_dtype,
src_dtype,
wrapper_fn,
new_ranges,
new_reduction_ranges,
reduction_type,
reduction_hint,
)
intermediate.realize()
intermediate_loader = intermediate.make_loader()
def intermediate_fn(index, reduction_index):
return intermediate_loader([*index, *reduction_index])
numel_hint = V.graph.sizevars.size_hint(sympy_product(original_ranges))
reduction_hint = cls._multilayer_second_step_hint(
split, numel_hint, reduction_hint
)
assert original_ranges == new_ranges[: len(original_ranges)]
return TensorBox.create(
Reduction(
device,
dst_dtype,
intermediate_fn,
original_ranges,
new_ranges[len(original_ranges) :],
reduction_type,
src_dtype,
reduction_hint,
)
)
@classmethod
def create_multilayer(
cls,
device: torch.device,
dst_dtype: torch.dtype,
src_dtype: torch.dtype,
inner_fn: Callable[..., Any],
ranges: List[Expr],
reduction_ranges: List[Expr],
reduction_type: str,
split: int,
reduction_hint: ReductionHint,
):
"""
Break a large reduction up into multiple smaller reductions
recursively
"""
# TODO(jansel): realize the reduction so we can do dynamic indexing
reduction_numel = sympy_product(reduction_ranges)
block_size = FloorDiv(reduction_numel + (split - 1), split)
default = cls.default_value(reduction_type, dst_dtype)
wrapper_fn = cls._multilayer_wrap_loader(
inner_fn, reduction_ranges, reduction_numel, split, block_size, default
)
return cls.create_multilayer_helper(
device,
dst_dtype,
src_dtype,
wrapper_fn,
ranges,
reduction_ranges,
[*ranges, split],
[block_size],
reduction_type,
split,
reduction_hint,
)
@classmethod
def create_multilayer_existing_ranges(
cls,
device: torch.device,
dst_dtype: torch.dtype,
src_dtype: torch.dtype,
inner_fn: Callable[..., Any],
original_ranges: List[Expr],
original_reduction_ranges: List[Expr],
new_ranges: List[Expr],
new_reduction_ranges: List[Expr],
reduction_type: str,
reduction_hint: ReductionHint,
):
"""
Break a large reduction up into multiple smaller reductions
recursively
"""
default = cls.default_value(reduction_type, dst_dtype)
wrapper_fn = cls._multilayer_wrap_loader_existing_ranges(
inner_fn,
original_ranges,
original_reduction_ranges,
new_ranges,
new_reduction_ranges,
default,
)
return cls.create_multilayer_helper(
device,
dst_dtype,
src_dtype,
wrapper_fn,
original_ranges,
original_reduction_ranges,
new_ranges,
new_reduction_ranges,
reduction_type,
-1,
reduction_hint,
)
def num_reduction_outputs(reduction_type):
return 3 if "welford" in reduction_type else 1
class WelfordReduction(Reduction):
output_index: int
def __init__(
self,
device,
dtype,
inner_fns,
ranges,
reduction_ranges,
reduction_type,
reduction_hint,
output_index,
):
if len(inner_fns) == 1:
loader = inner_fns[0]
else:
def loader(idx, reduction_idx):
return tuple(fn(idx, reduction_idx) for fn in inner_fns)
super().__init__(
device,
dtype,
loader,
ranges,
reduction_ranges,
reduction_type,
dtype,
reduction_hint,
)
self.output_index = output_index
def store_reduction(self, output_name, indexer, vars, reduction_vars):
values = ops.reduction(
self.dtype,
self.src_dtype,
self.reduction_type,
self.inner_fn(vars, reduction_vars),
)
value = values[self.output_index]
return ops.store_reduction(output_name, indexer(vars), value)
@classmethod
def create( # type: ignore[override]
cls,
device: torch.device,
dtype: torch.dtype,
inner_fns: Sequence[Callable[..., Any]],
ranges: List[Expr],
reduction_ranges: List[Expr],
reduction_type: str,
reduction_hint: ReductionHint = ReductionHint.DEFAULT,
):
assert reduction_type in {"welford_reduce", "welford_combine"}
reduction_numel = V.graph.sizevars.simplify(sympy_product(reduction_ranges))
def const(val):
def inner_fn(idx):
return ops.constant(
val,
dtype,
)
return Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=list(ranges),
)
if reduction_numel == 0:
mean = const(0)
m2 = const(0)
weight = const(0)
return mean, m2, weight
if reduction_numel == 1:
def copy(loader):
def inner_fn(idx):
reduction_index = [sympy.Integer(0) for _ in reduction_ranges]
return loader(idx, reduction_index)
return Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=list(ranges),
)
if reduction_type == "welford_reduce":
return copy(inner_fns[0]), const(0), const(1)
else:
return tuple(copy(fn) for fn in inner_fns)
# TODO: Unrolled reduction
# if (
# isinstance(reduction_numel, sympy.Integer)
# and V.graph.sizevars.size_hint(reduction_numel)
# < config.unroll_reductions_threshold
# and sympy_product(ranges) != 1
# ):
# return Pointwise.create(
# device,
# dst_dtype,
# cls._unroll_reduction_fn(
# inner_fn, reduction_ranges, reduction_type, src_dtype
# ),
# ranges,
# )
# triton doesn't support reduce to single element well, so break it up
hint, split = Reduction.num_splits(
device,
dtype,
dtype,
inner_fns[0],
ranges,
reduction_ranges,
reduction_type=reduction_type,
reduction_numel=reduction_numel,
)
# intermediate reduction in split can contain complex indexing,
# and num_splits will fail to correctly set the hint
# reuse the passed hint if available
if reduction_hint == ReductionHint.DEFAULT:
reduction_hint = hint
if split > 1:
# triton doesn't support reduce to single element well, so break it up
return cls.create_multilayer(
device,
dtype,
inner_fns,
ranges,
reduction_ranges,
reduction_type,
split,
reduction_hint,
)
results = [
TensorBox.create(
WelfordReduction(
device,
dtype,
inner_fns,
ranges,
reduction_ranges,
reduction_type,
reduction_hint,
output_idx,
)
)
for output_idx in range(3)
]
for t in results:
t.realize()
return results
@staticmethod
def default_value(reduction_type, dtype):
return (0, 0, 0)
@classmethod
def create_multilayer( # type: ignore[override]
cls,
device: torch.device,
dtype: torch.dtype,
inner_fns: Sequence[Callable[..., Any]],
ranges: List[Expr],
reduction_ranges: List[Expr],
reduction_type: str,
split: int,
reduction_hint: ReductionHint,
):
"""
Break a large reduction up into multiple smaller reductions
recursively
"""
reduction_numel = sympy_product(reduction_ranges)
need_mask = not V.graph.sizevars.is_expr_static_and_true(
sympy.Eq(reduction_numel % split, 0)
)
if need_mask and reduction_type != "welford_combine":
# If we need mask, then "welford_reduce" doesn't work because
# masked inputs shouldn't count towards the welford weight
def constant(idx, reduction_idx, value):
return ops.constant(value, dtype)
return cls.create_multilayer(
device=device,
dtype=dtype,
inner_fns=(
inner_fns[0],
partial(constant, value=0),
partial(constant, value=1),
),
ranges=ranges,
reduction_ranges=reduction_ranges,
reduction_type="welford_combine",
split=split,
reduction_hint=reduction_hint,
)
block_size = FloorDiv(reduction_numel + (split - 1), split)
intermediates = WelfordReduction.create(
device,
dtype,
tuple(
cls._multilayer_wrap_loader(
loader,
reduction_ranges,
reduction_numel,
split,
block_size,
default=0,
)
for loader in inner_fns
),
[*ranges, split],
[block_size],
reduction_type,
reduction_hint,
)
for i in intermediates:
i.realize()
i_loaders = [i.make_loader() for i in intermediates]
def intermediate_loader_fn(index, reduction_index, loader):
return loader([*index, *reduction_index])
numel_hint = V.graph.sizevars.size_hint(sympy_product(ranges))
reduction_hint = cls._multilayer_second_step_hint(
split, numel_hint, reduction_hint
)
return WelfordReduction.create(
device,
dtype,
tuple(
partial(intermediate_loader_fn, loader=i.make_loader())
for i in intermediates
),
ranges,
[split],
# welford_reduce turns one input into three outputs, which are combined with welford_combine
"welford_combine",
reduction_hint,
)
@dataclasses.dataclass
class Scan(Loops):
scan_ranges: List[Expr]
size: List[Expr]
combine_fn: Callable[..., Any]
reindex: Callable[[List[Expr], List[Expr]], List[Expr]]
reduction_hint: ReductionHint
init: Any
# HACK we mimick reduction
def __post_init__(self):
assert len(self.ranges) + len(self.scan_ranges) == len(self.size)
super().__post_init__()
def store_reduction(self, output_name, indexer, vars, scan_vars):
idx = self.reindex(vars, scan_vars)
value = self.inner_fn(idx)
result = ops.scan(self.dtype, self.combine_fn, value, self.init)
return ops.store(output_name, indexer(idx), result)
def get_reduction_type(self):
# return self.scan_op
return "custom"
def get_reduction_size(self):
return self.scan_ranges
def get_size(self):
return self.size
def get_pointwise_size(self):
return self.ranges
def index_length(self):
return len(self.ranges) + len(self.scan_ranges)
def inner_fn_str(self):
index = self._index(self.ranges)
rindex = self._index(self.scan_ranges, "r")
return V.KernelFormatterHandler.ir_to_string(
self.inner_fn,
index,
rindex,
)
@classmethod
def create(
cls,
device: torch.device,
dtype: torch.dtype,
inner_fn: Callable[[List[Expr]], Any],
size: List[Expr],
axis: int,
combine_fn: Callable[..., Any],
init: Any,
reduction_hint: ReductionHint = ReductionHint.DEFAULT,
) -> Optional["TensorBox"]:
pointwise_ranges = [*size[:axis], *size[axis + 1 :]]
scan_ranges = [size[axis]]
if device.type != "cuda":
# TODO: CPU support
return None
if torch.version.hip is not None:
# TODO: ROCm support
return None
sizevars = V.graph.sizevars
scan_numel = sizevars.simplify(sympy_product(scan_ranges))
# Scan with a single element is just a copy
if sizevars.is_expr_static_and_true(sympy.Le(scan_numel, 1)):
return Pointwise.create(
device=device,
dtype=dtype,
inner_fn=inner_fn,
ranges=size,
)
reduction_hint, num_splits = cls.num_splits(
device=device,
dtype=dtype,
inner_fn=inner_fn,
axis=axis,
pointwise_ranges=pointwise_ranges,
scan_ranges=scan_ranges,
combine_fn=combine_fn,
scan_numel=scan_numel,
)
if num_splits > 1:
# TODO: Support splitting
return None
def reindex(index, scan_index):
assert len(scan_index) == len(scan_ranges)
assert len(index) == len(pointwise_ranges)
return [*index[:axis], *scan_index, *index[axis:]]
result = TensorBox.create(
Scan(
device=device,
dtype=dtype,
inner_fn=inner_fn,
size=size,
ranges=pointwise_ranges,
scan_ranges=scan_ranges,
combine_fn=combine_fn,
reindex=reindex,
init=init,
reduction_hint=reduction_hint,
)
)
result.realize()
return result
@classmethod
def num_splits(
cls,
device: torch.device,
dtype: torch.dtype,
inner_fn: Callable[[List[Expr]], Any],
axis: int,
pointwise_ranges: List[Expr],
scan_ranges: List[Expr],
combine_fn: Callable[..., Any],
scan_numel: Expr,
):
# TODO: custom splitting heuristic for scan
def wrapper_fn(idx, reduction_idx):
return inner_fn([*idx[:axis], *reduction_idx, *idx[axis:]])
return Reduction.num_splits(
device=device,
dst_dtype=dtype,
src_dtype=dtype,
inner_fn=wrapper_fn,
ranges=pointwise_ranges,
reduction_ranges=scan_ranges,
reduction_type="sum",
reduction_numel=scan_numel,
)
def is_storage_and_layout(x):
try:
as_storage_and_layout(x, freeze=False)
return True
except NotImplementedError:
return False
def is_contiguous_storage_and_layout(x):
try:
buffer, layout = as_storage_and_layout(x, freeze=False)
return layout.is_contiguous()
except NotImplementedError:
return False
def as_storage_and_layout(x, freeze=True, want_contiguous=False, stride_order=None):
"""Try to simplify x into a StorageBox and a Layout"""
if isinstance(x, TensorBox):
return as_storage_and_layout(
x.data,
freeze=freeze,
want_contiguous=want_contiguous,
stride_order=stride_order,
)
if isinstance(x, StorageBox) and isinstance(x.data, Buffer):
if freeze:
if want_contiguous:
x.data.freeze_layout()
assert x.data.layout.is_contiguous()
elif stride_order is not None:
x.data.freeze_layout_with_stride_order(stride_order)
else:
x.data.decide_layout()
return x, x.data.layout
if isinstance(x, ReinterpretView):
# making the base of x contiguous or stride_ordered will not necessarily make
# the ReinterpretView either, so don't pass along those arguments
buffer, _ = as_storage_and_layout(
x.data,
freeze=freeze,
)
return buffer, x.layout
raise NotImplementedError
as_contiguous_storage_and_layout = functools.partial(
as_storage_and_layout, want_contiguous=True
)
def is_stride_order_storage_and_layout(x, stride_order):
try:
buffer, layout = as_storage_and_layout(x, freeze=False)
return layout.is_stride_ordered(stride_order)
except NotImplementedError:
return False
@dataclasses.dataclass
class BaseView(IRNode):
data: IRNode
def make_reindexer(self):
raise NotImplementedError(f"make_reindexer NYI on {self}")
def make_indexer(self):
inner = self.data.make_indexer()
reindex = self.make_reindexer()
def indexer(idx):
return inner(reindex(idx))
return indexer
def make_loader(self):
inner = self.data.make_loader()
reindex = self.make_reindexer()
def loader(idx):
return inner(reindex(idx))
return loader
def get_dtype(self):
return self.data.get_dtype()
def get_layout(self):
return self.data.get_layout()
def get_device(self):
return self.data.get_device()
def get_origin_node(self):
return None
def get_name(self):
return self.data.get_name()
def get_pointwise_size(self):
return self.get_size()
def mark_reuse(self, users):
return self.data.mark_reuse(users)
def has_exceeded_max_reads(self):
return self.data.has_exceeded_max_reads()
def realize(self):
return self.data.realize()
def realize_hint(self):
return self.data.realize_hint()
def get_storage_numel(self):
return self.data.get_storage_numel()
def is_extern(self):
return self.data.is_extern() # type: ignore[attr-defined]
def get_reads(self):
with patch.object(FlexibleLayout, "allow_indexing", True):
return extract_read_writes(
self.make_loader(),
self.get_size(),
).reads
def unwrap_view(self):
x: IRNode = self
while isinstance(x, BaseView):
x = x.data
return x
def constant_to_device(self, device):
"""Move this to a given device. Requires that all reads are to constants."""
loader = self.make_loader()
loader = patch.object(ConstantBuffer, "override_device", device)(loader)
return Pointwise(device, self.get_dtype(), loader, self.get_size())
@dataclasses.dataclass
class ExpandView(BaseView):
size: List[Expr]
@staticmethod
def _normalize_size(x, new_size):
"""Replace `-1` with correct sizes"""
new_size = list(map(sympy.expand, new_size))
old_size = x.get_size()
old_size = [None] * (len(new_size) - len(old_size)) + list(old_size)
assert len(new_size) == len(old_size)
for i in range(len(new_size)):
if new_size[i] == -1:
assert old_size[i] is not None
new_size[i] = old_size[i]
return new_size
@classmethod
def create(cls, x, new_size):
new_size = cls._normalize_size(x, new_size)
if is_storage_and_layout(x):
storage, old_layout = as_storage_and_layout(x)
skip = len(new_size) - len(old_layout.size)
assert skip >= 0
new_stride = [sympy.Integer(0)] * skip
for stride, size in zip(old_layout.stride, old_layout.size):
new_stride.append(stride if size != 1 else sympy.Integer(0))
new_layout = FixedLayout(
old_layout.device,
old_layout.dtype,
list(new_size),
new_stride,
old_layout.offset,
)
return ReinterpretView(storage, new_layout)
return ExpandView(x, new_size)
def get_size(self):
return self.size
def make_reindexer(self):
target = self.get_size()
actual = self.data.get_size()
skip = len(target) - len(actual)
def reindex(index):
index = list(index[skip:])
assert len(index) == len(actual)
for i in range(len(actual)):
if actual[i] == 1:
# zero out broadcast dimension
index[i] = sympy.Integer(0)
return index
return reindex
@dataclasses.dataclass
class PermuteView(BaseView):
dims: List[Expr]
@classmethod
def create(cls, x, dims):
dims = cls._map_neg_dims(dims)
assert set(dims) == set(range(len(dims)))
if is_storage_and_layout(x):
storage, old_layout = as_storage_and_layout(x)
new_layout = FixedLayout(
old_layout.device,
old_layout.dtype,
[old_layout.size[i] for i in dims],
[old_layout.stride[i] for i in dims],
old_layout.offset,
)
return ReinterpretView(storage, new_layout)
return PermuteView(x, dims)
@classmethod
def _map_neg_dims(cls, dims):
return [dim if dim >= 0 else len(dims) + dim for dim in dims]
def get_size(self):
assert set(self._map_neg_dims(self.dims)) == set(range(len(self.dims)))
size = self.data.get_size()
return [size[i] for i in self.dims]
def make_reindexer(self):
inv = {j: i for i, j in enumerate(self.dims)}
inv = [inv[i] for i in range(len(self.dims))]
assert set(inv) == set(range(len(self.dims)))
def reindex(index):
return [index[i] for i in inv]
return reindex
class SqueezeView(BaseView):
@classmethod
def create(cls, x, *, dim=None):
if is_storage_and_layout(x):
storage, old_layout = as_storage_and_layout(x)
new_size = []
new_stride = []
if dim is not None:
assert isinstance(dim, int), "expected integer dim argument"
assert 0 <= dim and dim < len(old_layout.size)
for i, (size, stride) in enumerate(zip(old_layout.size, old_layout.stride)):
if dim is None:
if size != 1:
new_size.append(size)
new_stride.append(stride)
else:
if i != dim:
new_size.append(size)
new_stride.append(stride)
else:
assert size == 1, "expected squeezed size to be 1"
new_layout = FixedLayout(
old_layout.device,
old_layout.dtype,
new_size,
new_stride,
old_layout.offset,
)
return ReinterpretView(storage, new_layout)
if dim is None:
# redirect to a generic view
return View.create(x, [s for s in x.get_size() if s != 1])
else:
assert x.get_size()[dim] == 1
return View.create(x, [s for i, s in enumerate(x.get_size()) if i != dim])
@staticmethod
def squeezer(size: Tuple[sympy.Expr, ...]):
new_size = [s for s in size if s != 1]
not_one = [i for i, s in enumerate(size) if s != 1]
length = len(size)
def reindex(index: List[sympy.Expr]) -> Tuple[sympy.Expr, ...]:
assert len(index) == len(not_one), f"{index} {not_one}"
new_index = [sympy.Integer(0)] * length
for idx, s in zip(not_one, index):
new_index[idx] = s
return tuple(new_index)
return new_size, reindex
def __init__(self, data):
raise AssertionError("use SqueezeView.create()")
@dataclasses.dataclass
class GenericView(BaseView):
size: List[Expr]
reindex: Callable[..., Any]
def make_reindexer(self):
return self.reindex
def reindex_str(self):
index_old = [sympy_symbol(f"i{n}") for n in range(len(self.size))]
index_new = list(self.reindex(index_old))
return f"lambda {', '.join(map(str, index_old))}: {index_new}"
def __str__(self):
return self.str_helper(
[self.data, f"size={self.size}", f"reindex={self.reindex_str()}"]
)
__repr__ = __str__
@classmethod
def create(cls, x, new_size, reindex):
return cls(x, list(new_size), reindex)
def get_size(self):
return self.size
@dataclasses.dataclass
class View(GenericView):
@staticmethod
def handle_negative_index(idx, size):
idx = sympy.expand(idx)
size = sympy.expand(size)
evaluate_expr = V.graph.sizevars.shape_env.evaluate_expr
if evaluate_expr(sympy.Lt(idx, 0)):
idx = idx + size
return idx
@classmethod
def create(cls, x, new_size):
assert isinstance(new_size, (tuple, list))
old_size, new_size = cls.resolve_negative_size(x.get_size(), new_size)
# Skip pointless views
if V.graph.sizevars.statically_known_list_equals(old_size, new_size):
return x
unbacked_symbols_in_sizes = False
if (
len(free_unbacked_symbols(old_size)) > 0
or len(free_unbacked_symbols(new_size)) > 0
):
unbacked_symbols_in_sizes = True
if 0 in new_size:
def fake_reindex(index):
return tuple([0] * len(old_size))
return cls(x, list(new_size), fake_reindex)
# TODO: a new class for FixedTransferLayout that output layout is constrained by input layout
elif is_contiguous_storage_and_layout(x) or unbacked_symbols_in_sizes:
if unbacked_symbols_in_sizes and (not is_contiguous_storage_and_layout(x)):
# realize x; otherwise, the dynamic_reshape_indexer below will fail
# due to the size_hint's inability to process unbacked SymInts
x.realize()
storage, old_layout = as_contiguous_storage_and_layout(x)
new_layout = FixedLayout(
old_layout.device,
old_layout.dtype,
new_size,
FlexibleLayout.contiguous_strides(new_size),
old_layout.offset,
)
return ReinterpretView(storage, new_layout)
reindex = cls.dynamic_reshape_indexer(old_size, new_size)
return cls(x, list(new_size), reindex)
@staticmethod
def resolve_negative_size(old_size, new_size):
new_size = [V.graph.sizevars.simplify(x) for x in new_size]
old_size = [V.graph.sizevars.simplify(x) for x in old_size]
new_size = list(new_size)
for i in range(len(new_size)):
if new_size[i] == -1:
new_size[i] = sympy.Integer(1)
new_size[i] = CleanDiv(sympy_product(old_size), sympy_product(new_size))
break
V.graph.sizevars.guard_equals(sympy_product(old_size), sympy_product(new_size))
return old_size, new_size
@classmethod
def dynamic_reshape_indexer(cls, old_size, new_size):
try:
reindex = cls._dynamic_reshape_indexer(old_size, new_size)
except (AssertionError, IndexError):
# optimistic algorithm failed, lets do a fallback
flat = [sympy_product(old_size)]
reindex1 = cls._dynamic_reshape_indexer(old_size, flat)
reindex2 = cls._dynamic_reshape_indexer(flat, new_size)
reindex = fuse_reindexing(reindex1, reindex2)
return reindex
@staticmethod
def _dynamic_reshape_indexer(old_size, new_size):
"""
Perform a reshape entirely by modifying indexing math
"""
size_hint = V.graph.sizevars.size_hint
vars = [sympy_symbol(f"view{i}") for i in range(len(new_size))]
stack_new = list(zip(vars, new_size))
stack_old = list(old_size)
view_expr = []
while stack_new and stack_old:
size_old = stack_old.pop()
var, size_new = stack_new.pop()
if size_old == 1:
view_expr.append(sympy.Integer(0))
stack_new.append((var, size_new)) # re-add
elif size_new == 1:
stack_old.append(size_old) # re-add
elif size_hint(size_new) == size_hint(size_old):
view_expr.append(var)
V.graph.sizevars.guard_equals(size_new, size_old)
elif size_hint(size_new) < size_hint(size_old):
while size_hint(size_new) < size_hint(size_old):
var2, size_new2 = stack_new.pop()
var = var2 * size_new + var
size_new = size_new * size_new2
view_expr.append(var)
V.graph.sizevars.guard_equals(size_new, size_old)
elif size_hint(size_new) > size_hint(size_old):
divisor = sympy.Integer(1)
modulus = size_old
view_expr.append(ModularIndexing(var, divisor, modulus))
divisor = divisor * modulus
while size_hint(size_new) > size_hint(size_old):
modulus = stack_old.pop()
view_expr.append(ModularIndexing(var, divisor, modulus))
divisor = divisor * modulus
size_old = size_old * modulus
V.graph.sizevars.guard_equals(size_new, size_old)
else:
raise AssertionError()
while stack_old:
size_old = stack_old.pop()
V.graph.sizevars.guard_equals(size_old, 1)
view_expr.append(sympy.Integer(0))
while stack_new:
var, size_new = stack_new.pop()
V.graph.sizevars.guard_equals(size_new, 1)
view_expr = list(reversed(view_expr))
assert len(view_expr) == len(old_size)
def reindex(index):
assert len(index) == len(vars), (len(index), len(vars))
replacements = dict(zip(vars, index))
return tuple(sympy_subs(x, replacements) for x in view_expr)
return reindex
@dataclasses.dataclass
class ReinterpretView(BaseView):
"""Pretend our storage has a different layout"""
layout: "Layout"
def __post_init__(self):
super().__post_init__()
if isinstance(self.data, BaseView):
self.data = self.data.unwrap_view()
def __str__(self):
return self.str_helper(
[
self.data,
self.layout,
]
)
__repr__ = __str__
def get_name(self):
return self.data.get_name()
def get_device(self):
return self.layout.device
def get_origin_node(self):
return None
def get_dtype(self):
return self.layout.dtype
def get_size(self):
return list(self.layout.size)
def get_stride(self):
return list(self.layout.stride)
def make_loader(self):
def loader(index):
indexer = self.layout.make_indexer()
return ops.load(self.get_name(), indexer(index))
return loader
def make_indexer(self):
return self.layout.make_indexer()
def get_layout(self):
return self.layout
def freeze_layout(self):
pass
def codegen_reference(self, writer=None):
# reinterpret_tensor is similar to as_strided except:
# - offset is added to the existing offset (rather than replacing it)
# - view tracking is disabled similar to unsafe_view
return V.graph.wrapper_code.codegen_reinterpret_view(
self.data,
self.layout.size,
self.layout.stride,
self.layout.offset,
writer,
)
class SliceView(View):
@classmethod
def create(cls, x, dim, start, end, step=1):
step = sympy.expand(step)
assert step > 0
try:
if start == 0 and end >= 2**63 - 1 and step == 1:
return x
except TypeError:
pass
sizevars = V.graph.sizevars
new_size = list(x.get_size())
start = cls.handle_negative_index(start, new_size[dim])
end = cls.handle_negative_index(end, new_size[dim])
if free_unbacked_symbols(start) or free_unbacked_symbols(end):
end = sympy.Min(end, new_size[dim])
start = sympy.Min(start, end)
else:
end = sizevars.evaluate_min(end, new_size[dim])
start = sizevars.evaluate_min(start, end)
new_size[dim] = FloorDiv(end - start + (step - 1), step)
if is_storage_and_layout(x):
# Fast path
storage, old_layout = as_storage_and_layout(x)
new_stride = list(old_layout.stride)
new_stride[dim] = new_stride[dim] * step
new_layout = FixedLayout(
old_layout.device,
old_layout.dtype,
new_size,
new_stride,
old_layout.offset + old_layout.stride[dim] * start,
)
return ReinterpretView(storage, new_layout)
def reindex(index):
assert len(index) == len(new_size), f"wrong ndim {index} {new_size}"
index = list(index)
index[dim] = index[dim] * step + start
return index
# redirect to a generic view
return SliceView(x, size=new_size, reindex=reindex)
class BaseConstant(IRNode):
dtype: torch.dtype
device: torch.device
def get_size(self):
return ()
def get_dtype(self):
return self.dtype
def get_device(self):
return self.device
def get_origin_node(self):
return None
def mark_reuse(self, users):
pass
def has_exceeded_max_reads(self):
return False
def get_reads(self):
return ()
def is_extern(self):
return False
@dataclasses.dataclass
class Constant(BaseConstant):
value: Any
dtype: torch.dtype
device: torch.device
def make_loader(self):
def loader(index):
return ops.constant(self.value, self.dtype)
return loader
def realize(self):
pass
def constant_to_device(self, device):
return Constant(self.value, self.dtype, device)
@dataclasses.dataclass
class IndexingConstant(BaseConstant):
index: Any
dtype: torch.dtype
device: torch.device
def make_loader(self):
def loader(index):
return ops.index_expr(self.index, self.dtype)
return loader
def constant_to_device(self, device):
return IndexingConstant(self.index, self.dtype, device)
def is_contiguous_strides_for_shape(stride, shape):
return all(
size == 1 or left == right
for left, right, size in zip(
stride, FlexibleLayout.contiguous_strides(shape), shape
)
)
@dataclasses.dataclass
class Layout(IRNode):
def __init__(
self,
device: torch.device,
dtype: torch.dtype,
size: List[Expr],
stride: Optional[Sequence[Union[Expr, int]]],
offset: Expr = Integer(0),
):
assert stride is None or len(size) == len(
stride
), f"size={size}, stride={stride}"
self.device = device
self.dtype = dtype
assert all(isinstance(s, (Expr, int)) for s in size)
self.size = size
self._stride = stride
self.offset = offset
@property
def stride(self):
return self._stride
def __str__(self):
offset = ""
if self.offset != 0:
offset = f", offset={self.offset}"
return (
f"{type(self).__name__}('{self.device.type}', {self.dtype}, "
f"size={self.size}, stride={self.stride}{offset})"
)
__repr__ = __str__
def is_contiguous(self):
return is_contiguous_strides_for_shape(self.stride, self.size)
def is_channels_last_contiguous(self):
ndim = len(self.size)
if ndim not in [4, 5]:
return False
for left, right, size in zip(
self.stride, make_channels_last_strides_for(self.size), self.size
):
if size != 1 and left != right:
return False
return True
def is_transposed(self):
for left, right, size in zip(
self.stride,
reversed(FlexibleLayout.contiguous_strides(self.size)),
self.size,
):
if size != 1 and left != right:
return False
return True
def is_stride_ordered(self, order):
assert len(self.stride) == len(order)
# ignore dimensions of size 1, they dont affect layout
non_1_indices = [
i
for i, dim in enumerate(self.size)
if V.graph.sizevars.size_hint(dim, fallback=2) != 1
]
stride = [self.stride[i] for i in non_1_indices]
order = [order[i] for i in non_1_indices]
def sorted_indices(arr):
sorted_arr = sorted(arr)
return [sorted_arr.index(element) for element in arr]
# since we may have removed dimensions, need to re-sort & re-index order
order = sorted_indices(order)
# reorder the stride given order
stride_ordered = [-1] * len(order)
for i in range(len(order)):
stride_ordered[order[i]] = V.graph.sizevars.size_hint(stride[i])
# check if it is in ascending order
for i in range(len(order) - 1):
if stride_ordered[i] > stride_ordered[i + 1]:
return False
return True
def is_channels_last_stride_ordered(self):
# create channels_last order(NCHW, NCDHW, the C is the first order).
order = [0] + list(reversed(range(1, len(self.stride) - 1)))
order = [len(order)] + order
return self.is_stride_ordered(order)
def as_fixed(self):
return FixedLayout(
self.device,
self.dtype,
self.size,
self.stride,
self.offset,
)
def make_indexer(self):
assert (
FlexibleLayout.allow_indexing
), f"convert {type(self).__name__} to FixedLayout first"
return self.as_fixed().make_indexer()
def __eq__(self, other) -> bool:
return (
self.device == other.device
and self.dtype == other.dtype
and self.size == other.size
and self.stride == other.stride
and self.offset == other.offset
)
def storage_size(self) -> sympy.Expr:
return compute_required_storage_length(self.size, self.stride, self.offset)
class FixedLayout(Layout):
"""A Tensor layout we cannot change"""
def __init__(
self,
device: torch.device,
dtype: torch.dtype,
size: Union[List[Expr], List[int]],
stride: Optional[Sequence[Union[Expr, int]]] = None,
offset: Union[Expr, int] = Integer(0),
):
if stride is None:
stride = FlexibleLayout.contiguous_strides(size)
super().__init__(
device,
dtype,
size,
stride,
offset,
)
def make_indexer(self):
"""A closure containing math to read a given element"""
def indexer(index):
assert len(index) == len(self.stride) == len(self.size)
result = self.offset
for idx, stride, sz in zip(index, self.stride, self.size):
if sz != 1:
result = result + idx * stride
return result
return indexer
class FlexibleLayout(Layout):
"""A Tensor layout we are allowed to change"""
allow_indexing = False
@staticmethod
def contiguous_strides(sizes):
if len(sizes) == 0:
return []
reversed_strides = [sympy.Integer(1)]
for size in reversed(sizes[1:]):
reversed_strides.append(size * reversed_strides[-1])
return list(reversed(reversed_strides))
@staticmethod
def fill_ordered(sizes, order):
"""
Create a stride based on the order the dimensions should be filled in.
In this format, channels last would be:
[1, 3, 2, 0]
"""
assert set(range(len(sizes))) == set(order)
next_stride = sympy.Integer(1)
strides = [None] * len(order)
for i in order:
strides[i] = next_stride
next_stride = next_stride * sizes[i]
return strides
@staticmethod
def stride_ordered(sizes, order):
"""
Create a stride based on the sorted order of a permuted range.
In this format, channels last would be:
[3, 0, 2, 1]
"""
assert set(range(len(sizes))) == set(order)
fill_order = stride_order2fill_order(order)
return FlexibleLayout.fill_ordered(sizes, fill_order)
@staticmethod
def same_ordered(sizes, stride):
"""
Create a stride that has the same stride order as given stride
For example, if given stride is [1000, 1, 100, 10],
the fill order should be [1, 3, 2, 0]
"""
assert len(sizes) == len(stride)
stride = [V.graph.sizevars.size_hint(x) for x in stride]
fill_order = sorted(range(len(stride)), key=stride.__getitem__)
return FlexibleLayout.fill_ordered(sizes, fill_order)
def as_stride_order(self, order):
return FixedLayout(
self.device,
self.dtype,
self.size,
self.stride_ordered(self.size, order),
self.offset,
)
def as_fill_order(self, order):
return FixedLayout(
self.device,
self.dtype,
self.size,
self.fill_ordered(self.size, order),
self.offset,
)
def as_same_order(self, stride):
return FixedLayout(
self.device,
self.dtype,
self.size,
self.same_ordered(self.size, stride),
self.offset,
)
def __init__(self, device, dtype, size, stride_order=None):
if stride_order:
strides = FlexibleLayout.fill_ordered(size, stride_order)
else:
strides = FlexibleLayout.contiguous_strides(size)
super().__init__(device, dtype, size, strides)
class AliasedLayout(Layout):
"""Shares the same storage as another tensor"""
def __init__(self, view: Union[BaseView, "TensorBox"]):
layout = view.get_layout()
super().__init__(
layout.device,
layout.dtype,
layout.size,
layout.stride,
)
self.view = view
def make_indexer(self):
return self.as_fixed().make_indexer()
def maybe_guard_aligned(self):
offset = self.view.get_layout().offset
if offset == 0:
return True
from .compile_fx import ALIGNMENT
return V.graph.sizevars.statically_known_multiple_of(offset, ALIGNMENT)
class NoneLayout(IRNode):
# This is janky, I figured out what fields to populate by just running
# the model I was interested in and adding properties/methods as needed.
# This doesn't inherit from Layout because Layout assumes you have stuff
# like sizes, but I don't really have anything here.
#
# If you have an ir.Node with NoneLayout, you probably need to setup
# dependencies manually in scheduler
def __init__(self, device):
self.device = device
self.size = [0]
self.stride = [0]
def storage_size(self):
return 0
def as_fixed(self):
return self
class MutationLayout(Layout):
def __init__(self, target: IRNode):
super().__init__(
target.get_device(),
target.get_dtype(),
target.get_size(),
None,
)
self.target = target
name = self.get_buffer().get_name()
V.graph.mark_buffer_mutated(name)
@Layout.stride.getter # type: ignore[attr-defined]
def stride(self):
return self.real_layout().stride
def storage_size(self) -> sympy.Expr:
return self.real_layout().storage_size()
def get_buffer(self) -> "Buffer":
def unwrap_views(target):
if isinstance(target, MutationLayout):
return unwrap_views(target.target)
if isinstance(target, BaseView):
return unwrap_views(target.unwrap_view())
if isinstance(target, MutableBox):
return unwrap_views(target.data)
return target
result = unwrap_views(self.target)
assert isinstance(result, Buffer), "MutationLayout must refer to a buffer"
return result
def real_layout(self):
return self.get_buffer().layout
@classmethod
def realize_into(cls, src, dst, unsafe_alias=False):
dst.realize()
# NOTE: We must realize users of `dst` before we realize `src`, since
# realization order determines scheduling order. Otherwise, src's
# mutation would be scheduled before the existing users of dst!
V.graph.mark_buffer_mutated(dst.get_name())
if isinstance(src, TensorBox):
src = src.data
# We copy the contents of src into dst. In most cases this should
# be fused into a single kernel by the scheduler.
# NOTE: We cannot change src's layout to mutate dst directly as this
# would alias src to dst, which is not correct as further mutations to
# dst would effect users of src. However if there are no more users of
# dst, we can alias src to dst.
src.realize_hint()
if not unsafe_alias:
src = Pointwise.create(
device=src.get_device(),
dtype=src.get_dtype(),
inner_fn=src.make_loader(),
ranges=[
V.graph.sizevars.guard_equals(a, b)
for a, b in zip(src.get_size(), dst.get_size())
],
).data
src.realize()
assert isinstance(src.data.layout, FlexibleLayout)
src.data.layout = MutationLayout(dst)
return src.data
def as_fixed(self):
return self
def make_indexer(self):
return self.target.make_indexer()
@dataclasses.dataclass
class Buffer(IRNode):
# Name is sometimes None; e.g., ForceInPlace, where there isn't
# a meaningful name
name: Optional[str]
layout: Layout
# Multi-output buffers will define 'outputs: List[Buffer]'. Confusingly,
# MultiOutput does NOT define this!
def __post_init__(self):
super().__post_init__()
self.origin_node = None
def make_indexer(self):
return self.layout.make_indexer()
def get_name(self) -> str:
assert self.name
return self.name
def get_device(self):
return self.layout.device
def get_origin_node(self):
return self.origin_node
def get_dtype(self):
return getattr(self.layout, "dtype", None)
def get_size(self):
return list(self.layout.size)
def get_stride(self):
return list(self.layout.stride)
def get_offset(self):
return self.layout.offset
def get_layout(self):
return self.layout
def get_storage_numel(self):
return self.get_numel()
def is_extern(self):
return False
def freeze_layout(self):
if not isinstance(self.layout, (MultiOutputLayout, AliasedLayout)):
self.layout = self.layout.as_fixed()
def freeze_layout_with_stride_order(self, order):
assert isinstance(self.layout, FlexibleLayout)
self.layout = self.layout.as_stride_order(order)
def freeze_layout_with_fill_order(self, order):
assert isinstance(self.layout, FlexibleLayout)
self.layout = self.layout.as_fill_order(order)
def freeze_layout_with_same_order(self, stride):
assert isinstance(self.layout, FlexibleLayout)
self.layout = self.layout.as_same_order(stride)
def is_zero_elements(self):
return V.graph.sizevars.is_expr_static_and_true(sympy.Eq(self.get_numel(), 0))
def make_loader(self):
# Loading from a zero-element buffer is a no-op
if self.is_zero_elements():
return partial(nop_loader_fn, dtype=self.get_dtype())
def loader(index):
indexer = self.layout.make_indexer()
return ops.load(self.name, indexer(index))
return loader
def is_no_op(self):
return False
def codegen_reference(self, writer=None):
return self.get_name()
def decide_layout(self):
pass
def get_alias_names(self):
if isinstance(self.layout, AliasedLayout):
return [self.layout.view.get_name()]
return ()
def get_mutation_names(self):
if isinstance(self.layout, MutationLayout):
return [self.layout.target.get_name()]
return ()
def get_read_writes(self):
with patch.object(FlexibleLayout, "allow_indexing", True):
return extract_read_writes(
self.make_loader(),
self.get_size(),
)
def get_reads(self):
return self.get_read_writes().reads
def get_unbacked_symbol_defs(self):
"""
Returns the unbacked symbols which are defined by this IR node,
because this is a data-dependent IR node, or item()
"""
# So this is a little unusual. In principle, you could imagine
# defining a MultiOutputLayout buffer so that it DOES define
# unbacked symints. However, we can't easily tell what symints
# such a buffer defines, because MultiOutputLayout doesn't actually
# define any useful information about what it returns.
#
# An easier and better approach is to delay the symint allocation
# to the MultiOutput IR nodes, which are when we actually extract
# out the buffers and know what their sizes are.
#
# There are two subleties here:
#
# 1. Suppose you have a kernel that produces out1: (i0,), out2: (i0,)
# Both of these actually count as defs! The scheduler will just
# arbitrarily pick one of these as the canonical definer and
# ensure it stays live. It's not a big deal if we pick the
# wrong one because tuple accesses are cheap, and all this means
# is we accidentally keep a MultiOutput node live when it wasn't
# strictly necessary.
#
# 2. Suppose you have a MultiOutput buffer whose size is (i0,), but
# the MultiOutputLayout buffer it is projecting from isn't actually
# dynamic; it has i0 as one of the arguments. We cannot tell this
# directly from MultiOutput, we have to look at the input buffer's
# uses to work this out. No big deal.
if isinstance(self.layout, (NoneLayout, MultiOutputLayout)):
return set()
# This kernel defines all unbacked symbols... that it didn't get in as
# arguments!
defs = (
free_unbacked_symbols(self.get_size())
| free_unbacked_symbols(self.get_stride())
| free_unbacked_symbols(self.get_offset())
)
return defs - self.get_unbacked_symbol_uses()
def get_unbacked_symbol_uses(self):
"""
Returns the unbacked symbols which are required to be in scope in
order to successfully perform codegen for this buffer. For example,
a buffer that corresponds to an extern kernel call that takes i0 as
an argument would return {i0} here. This is used to generate necessary
dependencies that ensure we actually bind i0 in codegen before you
try to use it.
Note that this is NOT transitive; in particular, if this buffer takes
in as input another buffer with dynamic shape (e.g., (i0,)), we will
not report it here, because you will already have a dependency
on that buffer, which will eventually have a dependency on i0 if
necessary.
"""
return set()
def codegen_unbacked_symbol_defs(self, wrapper):
# NB: If it is possible for other ir node types to return unbacked
# symints, you need to make sure their codegen calls this method.
# Don't forget to update get_unbacked_symbol_defs too.
symbols_to_define = self.get_unbacked_symbol_defs()
for i, s in enumerate(self.get_size()):
if s in symbols_to_define:
wrapper.writeline(
f"{wrapper.codegen_unbacked_symbol_decl(s)} = {self.get_name()}.size({i}){wrapper.ending}"
)
symbols_to_define.remove(s)
for i, s in enumerate(self.get_stride()):
if s in symbols_to_define:
wrapper.writeline(
f"{wrapper.codegen_unbacked_symbol_decl(s)} = {self.get_name()}.stride({i}){wrapper.ending}"
)
symbols_to_define.remove(s)
if (s := self.get_offset()) in symbols_to_define:
wrapper.writeline(
f"{wrapper.codegen_unbacked_symbol_decl(s)} = {self.get_name()}.storage_offset(){wrapper.ending}"
)
symbols_to_define.remove(s)
assert (
not symbols_to_define
), f"unbacked symint {s} not written out, check comment above"
def realize(self):
pass
def get_workspace_size(self):
"""
Gets extra global memory size needed by this buffer.
Some algorithms (e.g. group gemm) may require extra global memory in the generated code.
"""
return 0
def should_allocate(self):
# Returns False by default.
return False
class InputBuffer(Buffer):
pass
class ConstantBuffer(InputBuffer):
override_device: Optional[torch.device] = None
def make_loader(self):
def loader(index):
indexer = self.layout.make_indexer()
return ops.load(
V.graph.constant_name(self.get_name(), self.override_device),
indexer(index),
)
return loader
def constant_to_device(self, device):
return ConstantBuffer(
V.graph.constant_name(self.get_name(), device), self.layout
)
class NoneAsConstantBuffer(IRNode):
def codegen_reference(self, writer=None):
return V.graph.wrapper_code.none_str
class ShapeAsConstantBuffer(IRNode):
def __init__(self, shape):
super().__init__()
self.shape = shape
def codegen_reference(self, writer=None):
expr = V.graph.wrapper_code.expr_printer(V.graph.sizevars.simplify(self.shape))
if V.graph.cpp_wrapper:
# wrap scalar to 0-d tensor for cpp wrapper
return f"torch::tensor({expr})"
else:
return expr
@dataclasses.dataclass
class ComputedBuffer(Buffer):
data: Loops
def get_computed_buffer_name(self):
"""
Returns self.name if it exists, otherwise returns the name of the data node if that exists.
If neither exist, returns None.
"""
if self.name is not None:
return self.name
if hasattr(self.data, "name"):
return self.data.name
return None
@cache_on_self
def num_reads(self):
return len(self.get_read_writes().reads)
def get_read_writes(self):
with patch.object(FlexibleLayout, "allow_indexing", True):
if self.data.get_reduction_type():
return extract_read_writes(
self.get_store_function(),
self.data.get_pointwise_size(),
self.data.get_reduction_size(),
)
else:
return extract_read_writes(
self.get_store_function(),
self.data.get_size(),
)
def get_unbacked_symbol_uses(self):
# Ordinarily, we'd like to just peek at the arguments list,
# but ComputedBuffers have no argument list.
#
# Morally, this logic needs to be synchronized with the
# KernelArgs.size calls, which are responsible for making symbols make
# there way as kernel arguments (and it is precisely passing in one of
# those symbols that establishes a dependency). However, we haven't
# started codegen yet so we can't directly reuse that logic.
#
# For now, I'm just yoloing with the size of the buffer. Not sure if
# it is enough.
#
# One thing you might wonder is if this is enough for a ComputedBuffer
# denoting a reduction over i0. Empirically, it is enough, but for an
# unusual reason: we only need accurate dependencies for item() call,
# but it's impossible to end up with a reduction over i0 from an
# item() call without a regular non-reduction buffer first.
return (
free_unbacked_symbols(self.get_size())
| free_unbacked_symbols(self.get_stride())
| free_unbacked_symbols(self.get_offset())
)
def make_loader(self):
# Inline constants and index_expressions
if (
hasattr(self.data, "make_loader")
and self.name not in V.graph.mutated_buffers
and self.num_reads() == 0
):
# can be inlined
return self.data.make_loader()
return super().make_loader()
def get_store_function(self):
indexer = self.layout.as_fixed().make_indexer()
if isinstance(self.data, (Reduction, Scan)):
return partial(self.data.store_reduction, self.name, indexer)
else:
assert isinstance(self.data, Pointwise)
return partial(self.data.store_output, self.name, indexer)
def get_fill_order(self):
"""
If our layout is still flexible, try to determine the stride order based on stride orders of reads.
TODO(jansel): A better algorithm here would look at downstream consumers of this
value and try to do global graph-level layout optimization.
This is also something just begging to be autotuned.
"""
if isinstance(self.layout, FlexibleLayout):
(index_vars, reduction_vars), _ = dependencies.index_vars_squeeze(
self.data.get_pointwise_size(), self.data.get_reduction_size()
)
reads = self.get_read_writes().reads
reads_bufs = [
V.graph.name_to_buffer[r.name]
if r.name in V.graph.name_to_buffer.keys()
else None
for r in reads
]
# only consider reads to buffer of same size
# ignore StarDeps because they don't contribute stride information
assert all(
isinstance(r, (dependencies.StarDep, dependencies.MemoryDep))
for r in reads
)
reads = [
sympy_subs(
r.index, {v: sympy.Integer(0) for v in reduction_vars if v != 0}
)
for r in reads
if isinstance(r, dependencies.MemoryDep)
]
if reads:
if isinstance(self.data, Scan):
indices = self.data.reindex(index_vars, reduction_vars)
else:
indices = index_vars
stride_lengths = [
V.graph.sizevars.stride_hints(expr, indices) for expr in reads
]
from .scheduler import pick_loop_order
return pick_loop_order(stride_lengths, self.get_size())
return None
def decide_layout(self):
if isinstance(self.layout, FlexibleLayout):
order = self.get_fill_order()
if order:
self.freeze_layout_with_fill_order(order)
else:
self.freeze_layout()
def simplify_and_reorder(self):
"""
This is a main place where we do loop transformations in a
backend-agnostic way.
Here we:
1) Remove any 1 dimensions
2) Fuse contiguous dimensions together
3) Reorder dimensions based on stride orders
"""
args, var_ranges = dependencies.index_vars_squeeze(
self.data.get_pointwise_size(), self.data.get_reduction_size(), prefix="q"
)
with patch.object(ConstantBuffer, "override_device", self.get_device()):
body = LoopBody(
self.get_store_function(),
(args if self.get_reduction_type() else args[:1]),
var_ranges,
)
index_formulas = [*body.indexing_exprs.values()]
reads_bufs = [
V.graph.name_to_buffer[reads_name]
if reads_name in V.graph.name_to_buffer.keys()
else None
for reads_name in body.reads_name2expr.keys()
]
memory_addrs = [
*body.reads_name2expr.values(),
*body.writes_name2expr.values(),
]
index_vars = []
reduce_vars: List[Any] = []
index_size = []
reduce_size = []
for v, s in var_ranges.items():
if v in args[0]:
assert not reduce_vars
index_vars.append(v)
index_size.append(s)
else:
assert v in args[1]
reduce_vars.append(v)
reduce_size.append(s)
# the reordering_reindex in reads' simplify_reorder_and_tile
reordering_reindex = [same_reorder(range(len(index_vars)))] * len(memory_addrs)
for i, reads_buf in enumerate(reads_bufs):
if isinstance(reads_buf, ComputedBuffer) and hasattr(
reads_buf, "iter_reordering_reindex"
):
reordering_reindex[i] = reads_buf.iter_reordering_reindex # type: ignore[has-type]
def simplify_and_reorder(x_vars, support_vars, sizes, reordering_reindex=None):
sizes, reindex0, reindex1 = self._apply_loop_reordering(
x_vars, support_vars, sizes, memory_addrs, reordering_reindex
)
# for NHWC: reindex0([0,1,2,3]) = [0,2,3,1], reindex1([0,1,2,3]) = [0,3,2,1]
x_vars = reindex0(x_vars)
sizes, reindex2, prune = V.graph.sizevars._simplify_loops(
x_vars,
sizes,
index_prevent_reordering(index_formulas, x_vars, sizes),
)
x_vars = prune(x_vars)
# sizes, reindex1, prune = _simplify_loops(x_vars, sizes, index_formulas)
# x_vars = prune(x_vars)
# sizes, reindex2 = self._apply_loop_reordering(x_vars, sizes, memory_addrs)
reindex = fuse_reindexing(reindex1, reindex2)
return sizes, reindex, reindex1
support_vars = index_vars + reduce_vars
iter_ranges, iter_reindex, iter_reordering_reindex = simplify_and_reorder(
index_vars, support_vars, index_size, reordering_reindex
)
reduce_ranges, reduce_reindex, _ = simplify_and_reorder(
reduce_vars, support_vars, reduce_size
)
# remember the reordering if not have loop collapse.
if len(iter_ranges) == len(index_vars):
self.iter_reordering_reindex = iter_reordering_reindex
# retrace the loop body with simplification and reordering applied
(iter_vars, reduce_vars), var_ranges = dependencies.index_vars_no_squeeze(
iter_ranges, reduce_ranges, prefix="z"
)
body = LoopBody(
body, [iter_reindex(iter_vars), reduce_reindex(reduce_vars)], var_ranges
)
return (iter_ranges, reduce_ranges), body
@staticmethod
def _apply_loop_reordering(
index_vars,
support_vars,
sizes,
memory_addrs,
reordering_reindex=None,
priority_idx=None,
):
"""
Shuffle the order of loops around to hopefully improve performance.
"""
from .scheduler import pick_loop_order
if priority_idx is None:
priority_idx = []
try:
strides = [
V.graph.sizevars.stride_hints(expr, index_vars, support_vars)
for expr in memory_addrs
]
assert len(strides) == len(memory_addrs) and len(strides[0]) == len(
index_vars
)
# consider both layout(strides) and reordering(reordering_reindex)
if reordering_reindex is not None:
for i in range(len(memory_addrs)):
try:
strides[i] = reordering_reindex[i](strides[i])
# if len(order) != len(strides), do not reorder
except AssertionError:
pass
order = list(reversed(pick_loop_order(strides, sizes, priority_idx)))
except Exception:
if config.debug:
log.warning(
"Did not simplify complex index:\n%s\n%s",
dict(zip(index_vars, sizes)),
memory_addrs,
)
order = list(range(len(sizes)))
sizes = [sizes[i] for i in order]
return sizes, same_reorder(order), inverse_reorder(order)
def get_reduction_size(self):
return self.data.get_reduction_size()
def get_reduction_type(self):
return self.data.get_reduction_type()
def is_no_op(self):
return self.data.is_zero_elements()
def should_allocate(self):
return True
def constant_to_device(self, device):
"""Move this to a given device. Requires that all reads are to constants."""
return self.data.constant_to_device(device)
class TemplateBuffer(Buffer):
"""
Represents a Triton (in the future other type) of template operator
that we can fuse an epilogue onto.
"""
def __init__(self, layout, inputs, make_kernel_render):
super().__init__(name=None, layout=layout)
self.inputs = InputsKernel.unwrap_storage(inputs)
self.make_kernel_render = make_kernel_render
self.name = V.graph.register_buffer(self)
def get_read_writes(self):
return self.normalized_read_writes()
def normalized_read_writes(self):
name = self.get_name()
indexer = self.layout.make_indexer()
def dummy(index, rindex):
assert len(rindex) == 0
return ops.store(name, indexer(index), "fake")
deps = dependencies.extract_read_writes(
dummy, self.get_size(), (), normalize=True
)
deps.reads = {dependencies.StarDep(x.get_name()) for x in self.inputs}
return deps
def get_reduction_size(self):
return 1
def get_reduction_type(self):
return None
def is_no_op(self):
return False
def should_allocate(self):
return True
def simplify_and_reorder(self):
return (
(
self.get_size(),
(),
),
None,
)
class TritonTemplateBuffer(TemplateBuffer):
pass
class CUDATemplateBuffer(TemplateBuffer):
def __init__(
self,
layout,
inputs,
make_kernel_render,
workspace_size: int,
template: "CUDATemplate", # type: ignore[name-defined]
):
super().__init__(layout, inputs, make_kernel_render)
# Global memory (in bytes) needed for this template.
self.workspace_size = workspace_size
self.template = template
def get_workspace_size(self):
return self.workspace_size if self.workspace_size is not None else 0
@dataclasses.dataclass
class InputsKernel(Buffer):
inputs: List[Buffer]
def get_read_writes_input(self, x):
return dependencies.StarDep(x.get_name())
def get_read_writes(self):
star_dep = []
for input in self.inputs:
if isinstance(input, list):
star_dep.extend([self.get_read_writes_input(x) for x in input])
else:
star_dep.append(self.get_read_writes_input(input))
return dependencies.ReadWrites(
set(star_dep),
{dependencies.StarDep(self.get_name())},
set(),
[],
None,
op_counts=collections.Counter(),
)
@staticmethod
def unwrap_storage_for_input(x):
if isinstance(x, TensorBox):
x = x.data
if isinstance(x, StorageBox):
x = x.data
if isinstance(x, BaseView) and not isinstance(x, ReinterpretView):
x = ExternKernel.realize_input(x)
assert isinstance(x, (Buffer, ReinterpretView)), x
return x
@staticmethod
def unwrap_storage(inputs):
inputs_new = []
for x in inputs:
if isinstance(x, list):
x = [InputsKernel.unwrap_storage_for_input(i) for i in x]
else:
x = InputsKernel.unwrap_storage_for_input(x)
inputs_new.append(x)
return inputs_new
def is_extern(self):
return True
class NopKernel(InputsKernel):
def is_no_op(self):
return True
class ConcatKernel(NopKernel):
"""
There isn't actually a real kernel for concat, we just change the
storage for the upstream data.
"""
@classmethod
def create(cls, inputs, dim):
device = inputs[0].get_device()
dtype = inputs[0].get_dtype()
new_size = list(inputs[0].get_size())
offsets_start = [0]
offsets_end = [new_size[dim]]
assert 0 <= dim < len(new_size)
for i in range(1, len(inputs)):
input_size = inputs[i].get_size()
offsets_start.append(new_size[dim])
assert len(input_size) == len(new_size)
assert inputs[i].get_dtype() == dtype
assert inputs[i].get_device() == device
for j in range(len(new_size)):
if j == dim:
new_size[j] = new_size[j] + input_size[j]
else:
new_size[j] = V.graph.sizevars.guard_equals(
new_size[j], input_size[j]
)
offsets_end.append(new_size[dim])
output_stride = FlexibleLayout.contiguous_strides(new_size)
# If any of the inputs is in CL format, use CL format for the output
for i in range(len(inputs)):
x = inputs[i]
if is_storage_and_layout(x):
layout = x.get_layout()
if (
isinstance(layout, FixedLayout)
and layout.is_channels_last_contiguous()
):
# use CL stride for the output
output_stride = make_channels_last_strides_for(new_size)
break
concat_kernel = ConcatKernel(
name=None,
layout=FixedLayout(
device=device,
dtype=dtype,
size=new_size,
stride=output_stride,
),
inputs=[],
)
kernel = StorageBox(concat_kernel)
buffer_names = []
for i in range(len(inputs)):
input_buffer = cls.realize_into(
inputs[i],
SliceView.create(kernel, dim, offsets_start[i], offsets_end[i]),
)
concat_kernel.inputs.append(input_buffer)
if isinstance(inputs[i].data, BaseView):
input_unwrapped = inputs[i].data.unwrap_view()
else:
input_unwrapped = inputs[i].data
if (
input_unwrapped.is_input_buffer()
and inputs[i].get_device().type == "cuda"
and not is_dynamic(input_buffer)
):
buffer_names.append(input_buffer.get_name())
if len(buffer_names) > 1:
V.graph.register_list(buffer_names)
concat_kernel.name = V.graph.register_buffer(concat_kernel)
concat_kernel.inputs = cls.unwrap_storage(concat_kernel.inputs)
return kernel
@classmethod
def can_realize_into_without_copy(cls, src):
if isinstance(src, TensorBox):
# unwrap a TensorBox
return cls.can_realize_into_without_copy(src.data)
return isinstance(src.data.layout, FlexibleLayout) and not isinstance(
src.data, ExternKernelAlloc
)
@classmethod
def realize_into(cls, src, dst):
# Attempt to turn this into a ReinterpretView rather than assert.
# This has concessions around layout, as as_storage_and_layout
# can cause us to go from flexible to fixed layout.
if not isinstance(dst, ReinterpretView):
if is_storage_and_layout(dst):
storage, layout = as_storage_and_layout(dst)
dst = ReinterpretView(storage, layout)
assert isinstance(dst, ReinterpretView), dst
if isinstance(src, TensorBox):
# unwrap a TensorBox
return cls.realize_into(src.data, dst)
if isinstance(src, StorageBox):
src.realize()
# ExternKernelAlloc has specific requirements for output layout, should create a copy
assert hasattr(src.data, "layout")
if cls.can_realize_into_without_copy(src):
src.data.layout = AliasedLayout(dst)
return src.data
# introduce a copy
pw = Pointwise.create(
device=src.get_device(),
dtype=src.get_dtype(),
inner_fn=src.make_loader(),
ranges=[
V.graph.sizevars.guard_equals(a, b)
for a, b in zip(src.get_size(), dst.get_size())
],
)
return cls.realize_into(pw, dst)
def should_allocate(self):
return True
@dataclasses.dataclass
class ExternKernel(InputsKernel):
constant_args: Tuple[Any, ...] = ()
kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)
output_view: Optional[ReinterpretView] = None
ordered_kwargs_for_cpp_kernel: Iterable[str] = dataclasses.field(
default_factory=list
)
def decide_layout(self):
if isinstance(self.layout, FlexibleLayout):
self.apply_constraint()
self.freeze_layout()
def codegen_comment(self, wrapper):
origin_str, detailed_origin_str = get_kernel_metadata(self, wrapper)
if origin_str:
wrapper.writeline(origin_str)
def codegen(self, wrapper):
raise NotImplementedError()
@staticmethod
def copy_input(x):
pw = Pointwise.create(
device=x.get_device(),
dtype=x.get_dtype(),
inner_fn=x.make_loader(),
ranges=x.get_size(),
origin_node=x.get_origin_node(),
traceback=x.get_traceback(),
)
pw.realize()
return pw
@classmethod
def process_kernel(cls, kernel, *args, **kwargs):
binded_args = signature(kernel).bind(*args, **kwargs).arguments
args_flat, args_spec = pytree.tree_flatten(binded_args)
is_arg_tensor = []
tensor_args = []
non_tensor_args: List[Any] = []
for arg in args_flat:
is_arg_tensor.append(isinstance(arg, IRNode))
if is_arg_tensor[-1]:
tensor_args.append(arg)
else:
if isinstance(arg, sympy.Expr):
arg = V.graph.sizevars.shape_env.create_symintnode(arg, hint=None)
non_tensor_args.append(arg)
def unflatten_args(new_tensor_args, new_non_tensor_args):
result = []
it_tensors = iter(new_tensor_args)
it_non_tensors = iter(new_non_tensor_args)
for is_tensor in is_arg_tensor:
if is_tensor:
result.append(next(it_tensors))
else:
result.append(next(it_non_tensors))
r = pytree.tree_unflatten(result, args_spec)
return r.get("args", []), r.get("kwargs", {})
tensor_args = [cls.realize_input(x) for x in tensor_args]
# freeze layout otherwise our output stride calculation might
# become incorrect
for x in tensor_args:
if is_storage_and_layout(x):
as_storage_and_layout(x, freeze=True)
# We don't have generic shape formulas, so just burn in the
# shapes and run an example input.
# TODO(jansel): replace this with dynamic shape formulas
example_args = []
# We need to retain the constant values of fake tensors that we originally
# propagated the graph with, because for some operators running without a
# constant would trigger an error / DataDependentException
for x in tensor_args:
if x.get_name() in V.graph.constants:
example_args.append(V.graph.constants[x.get_name()])
else:
example_args.append(ir_node_to_tensor(x, guard_shape=True))
new_args, new_kwargs = unflatten_args(example_args, non_tensor_args)
example_output = kernel(*new_args, **new_kwargs)
example_out_li = (
[example_output]
if not isinstance(example_output, (list, tuple))
else example_output
)
for t in example_out_li:
if isinstance(t, torch.Tensor) and t.is_sparse:
V.graph.disable_cudagraphs = True
msg = "sparsity not handled. Please file issue for sparse inference weights."
if stack_trace := V.graph.current_node.meta.get("stack_trace", None):
msg = f"{msg} Found from : \n {stack_trace}"
V.graph.disable_cudagraphs_reason = msg
# TODO: Unconditionally do this, not just when example_output has
# unbacked symbols
if maybe_free_unbacked_symbols(example_output):
example_output = V.graph.current_node.meta["val"]
return example_output, tensor_args, non_tensor_args, unflatten_args
@classmethod
def convert_to_reinterpret_view(cls, x):
"""
In order to pass this to an extern kernel we need a
ReinterpretView not a View. This allows us to avoid some
unneeded copies.
"""
assert isinstance(x, BaseView)
if isinstance(x, ReinterpretView):
return x
# NOTE: Don't use extract_read_writes here as it fails when
# make_loader() inlines the computation
x.unwrap_view().freeze_layout()
index_args, var_ranges = dependencies.index_vars_squeeze(
x.get_size(), prefix="r"
)
range_vars = index_args[0]
index = x.make_indexer()(range_vars)
index = V.graph.sizevars.simplify_with_ranges(index, var_ranges)
strides = V.graph.sizevars.stride_vars(index, range_vars)
offset = V.graph.sizevars.offset_var(index, range_vars)
expected = sympy_dot(range_vars, strides) + offset
if index != expected:
log.debug(
"convert_to_reinterpret_view failed: stride=%s offset=%s index=%s",
strides,
offset,
index,
)
raise NotImplementedError()
return ReinterpretView(
data=x.data,
layout=FixedLayout(
device=x.get_device(),
dtype=x.get_dtype(),
size=x.get_size(),
stride=strides,
offset=offset,
),
)
@classmethod
def realize_input(cls, x):
if x is None:
return NoneAsConstantBuffer()
if isinstance(x, (sympy.Expr, sympy.logic.boolalg.Boolean, int)):
return ShapeAsConstantBuffer(x)
if isinstance(x, Constant):
return V.graph.add_tensor_constant(
torch.tensor(x.value, dtype=x.get_dtype(), device=x.get_device())
)
if isinstance(x, ConstantBuffer):
return x
if isinstance(x, TensorBox):
return cls.realize_input(x.data)
if isinstance(x, ReinterpretView):
return x
if isinstance(x, BaseView):
x.realize()
if is_storage_and_layout(x.unwrap_view()):
try:
return cls.convert_to_reinterpret_view(x)
except NotImplementedError:
pass
if isinstance(x, StorageBox):
# TODO(jansel): impose layout preference on realized buffer
x.realize()
return x
return cls.copy_input(x)
@classmethod
def require_stride1(cls, x):
if is_storage_and_layout(x):
if len(x.get_stride()) == 0:
return x
for stride in x.get_stride():
if stride == 1:
return x
return cls.copy_input(x)
@classmethod
def require_stride_order(cls, x, order):
if x.get_numel() == 0: # Layout doesn't matter
return x
# require x to have the layout as strided_ordered as order
if is_storage_and_layout(x):
while isinstance(x.get_layout(), AliasedLayout):
x = x.get_layout().view
if isinstance(x.get_layout(), FlexibleLayout):
# fix flexiblelayout to be FixedLayout with stride_order
as_storage_and_layout(
x, freeze=True, want_contiguous=False, stride_order=order
)
return x
elif isinstance(
x.get_layout(), FixedLayout
) and x.get_layout().is_stride_ordered(order):
return x
elif isinstance(x.get_layout(), MutationLayout):
if isinstance(x.get_layout().real_layout(), FlexibleLayout):
raise AssertionError(
"the MutationLayout's real layout shouldn't be FlexibleLayout"
)
elif isinstance(
x.get_layout().real_layout(), FixedLayout
) and x.get_layout().real_layout().is_stride_ordered(order):
return x
# TODO - Storage to InputBuffer
if isinstance(x, InputBuffer) and x.get_layout().is_stride_ordered(order):
return x
if (
isinstance(x, TensorBox)
and isinstance(x.data, BaseView)
and not isinstance(x.data, ReinterpretView)
and is_storage_and_layout(x.unwrap_view())
and not isinstance(x.unwrap_view().data, ExternKernelAlloc)
):
try:
x.data = cls.convert_to_reinterpret_view(x.data)
return cls.require_stride_order(x, order)
except NotImplementedError:
pass
x = cls.copy_input(x)
as_storage_and_layout(x, freeze=True, want_contiguous=False, stride_order=order)
assert is_stride_order_storage_and_layout(x, order)
return x
@classmethod
def require_channels_last(cls, x):
return cls.require_stride_order(x, NHWC_STRIDE_ORDER)
@classmethod
def require_contiguous(cls, x):
return cls.require_stride_order(x, list(reversed(range(len(x.get_size())))))
def apply_constraint(self):
pass
def codegen_const_args(self):
return map(V.graph.wrapper_code.val_to_arg_str, self.constant_args)
def codegen_args(self):
args = []
for x in self.inputs:
if isinstance(x, list):
names = [i.codegen_reference() for i in x]
codegen_reference = f'[{", ".join(names)}]'
args.append(codegen_reference)
else:
args.append(x.codegen_reference())
args.extend(self.codegen_const_args())
return args
def get_kwargs_value(self, arg_name):
if arg_name in self.kwargs:
return self.kwargs.get(arg_name)
if (
hasattr(self, "kwargs_default_value")
and arg_name in self.kwargs_default_value
):
return self.kwargs_default_value.get(arg_name).get("value")
raise AssertionError(
f"arg {arg_name} not found in self.kwargs or self.kwargs_default_value"
)
def is_legacy_abi_kernel(self):
return False
def codegen_kwargs(self):
if V.graph.cpp_wrapper:
# FIXME: we should unconditionally fill self.kwargs with missing default values
# instead of carrying an extra self.ordered_kwargs_for_cpp_kernel
if self.kwargs and not self.ordered_kwargs_for_cpp_kernel:
raise AssertionError("ordered_kwargs_for_cpp_kernel is missing")
kwargs = []
for arg_name in self.ordered_kwargs_for_cpp_kernel:
v = self.get_kwargs_value(arg_name)
if isinstance(v, sympy.Expr):
kwargs.append(v)
else:
# FIXME We should let ExternKernel have access to the cpp schema where possible.
if hasattr(self, "kwargs_default_value"):
type_ = self.kwargs_default_value.get(arg_name).get("type")
else:
type_ = None
kwargs.append(
V.graph.wrapper_code.val_to_cpp_arg_str(
type_, v, self.is_legacy_abi_kernel()
)
)
else:
kwargs = [
f"{k}={V.graph.wrapper_code.val_to_arg_str(v)}"
for k, v in self.kwargs.items()
]
return kwargs
def codegen_size_asserts(self, wrapper):
if config.size_asserts and not V.graph.cpp_wrapper:
size = V.graph.wrapper_code.codegen_shape_tuple(self.get_size())
stride = V.graph.wrapper_code.codegen_shape_tuple(self.get_stride())
wrapper.writeline(
f"assert_size_stride({self.get_name()}, {size}, {stride})"
)
def get_group_stride(self):
"""
get output sizes and strides, for template_codegen
"""
_size = self.get_size()
_stride = self.get_stride()
# iter_ranges = _size of output tensor, reduce_range = [] because no reduction
return [_size, []], _stride
def canonicalize(self):
"""
Manually get canonicalization of the output index
"""
# manually generate index formula for conv
sizevars = V.graph.sizevars
sizes = self.get_size()
strides = self.get_stride()
strides = [sizevars.size_hint(x) for x in strides]
index_vars = [sympy_symbol(f"d{i}") for i in range(len(sizes))]
# reorder index vars according to stride
index_order = sorted(range(len(strides)), key=strides.__getitem__, reverse=True)
lookup = {pos: idx for idx, pos in enumerate(index_order)}
order = [lookup[i] for i in range(len(lookup))]
index_vars = [index_vars[i] for i in order]
indexer = self.make_indexer()
index = indexer(index_vars)
new_sizes, reindex, prune = V.graph.sizevars._simplify_loops(
index_vars, sizes, [index]
)
# assign new variables each dimension to deal with numbering mismatches
# d0, d1, d2 could become d0, d2 -- which won't match d0, d1
_, add_var = var_builder("c")
replacement = dict(zip(index_vars, reindex([add_var(x) for x in new_sizes])))
index = sympy_subs(sympy.expand(index), replacement)
return index, tuple(new_sizes)
def get_unbacked_symbol_uses(self):
# NB: It's not necessary to check regular inputs as we automatically
# have dependencies on them
r = set()
for arg in self.constant_args:
r |= maybe_free_unbacked_symbols(arg)
for arg in self.kwargs.values():
r |= maybe_free_unbacked_symbols(arg)
return r
def __str__(self):
kernel_name = getattr(self, "kernel", None)
lines = [
f"kernel={kernel_name!r}",
]
lines += [
f"{field.name}={getattr(self, field.name)}"
for field in dataclasses.fields(self)
]
lines.append(f"origin_node={self.origin_node!r}")
return self.str_helper(lines)
__repr__ = __str__
@dataclasses.dataclass
class ExternKernelOut(ExternKernel):
output_view: Optional[ReinterpretView] = None
def codegen(self, wrapper):
self.codegen_comment(wrapper)
args = [*self.codegen_args(), *self.codegen_kwargs()]
wrapper.generate_extern_kernel_out(
self.output_view,
self.codegen_reference(),
args,
self.cpp_kernel if V.graph.cpp_wrapper else self.kernel,
)
def __init__(
self,
layout,
inputs,
constant_args=(),
kwargs=None,
output_view=None,
kernel=None,
cpp_kernel=None,
ordered_kwargs_for_cpp_kernel=(),
):
super().__init__(
None, layout, self.unwrap_storage(inputs), constant_args, kwargs or {}
)
self.output_view = output_view
self.name = V.graph.register_buffer(self)
self.kernel = kernel
self.cpp_kernel = cpp_kernel
self.ordered_kwargs_for_cpp_kernel = ordered_kwargs_for_cpp_kernel
def should_allocate(self):
return True
class RandomSeeds(ExternKernelOut):
def __init__(self, count: int, device: torch.device):
limits = torch.iinfo(torch.int64)
super().__init__(
layout=FixedLayout(
device=device,
dtype=torch.int64,
size=[count],
),
inputs=[],
constant_args=[limits.min, limits.max, [count]],
kernel="aten.randint.low_out",
cpp_kernel="at::randint_out",
)
class ExternKernelAlloc(ExternKernel):
def codegen_kernel_name(self):
return self.cpp_kernel if V.graph.cpp_wrapper else self.kernel
def codegen(self, wrapper):
self.codegen_comment(wrapper)
args = [*self.codegen_args(), *self.codegen_kwargs()]
V.graph.wrapper_code.generate_extern_kernel_alloc(self, args)
if isinstance(self.layout, Layout):
self.codegen_size_asserts(wrapper)
def __init__(
self,
layout,
inputs,
constant_args=(),
kwargs=None,
kernel=None,
cpp_kernel=None,
ordered_kwargs_for_cpp_kernel=(),
):
super().__init__(
None, layout, self.unwrap_storage(inputs), constant_args, kwargs or {}
)
self.name = V.graph.register_buffer(self)
self.kernel = kernel
self.cpp_kernel = cpp_kernel
self.ordered_kwargs_for_cpp_kernel = ordered_kwargs_for_cpp_kernel
def should_allocate(self):
return False
def apply_constraint(self):
raise NotImplementedError
class UserDefinedTritonKernel(ExternKernel):
def get_kernel_and_configs(self):
from triton.runtime.autotuner import Autotuner
from torch._higher_order_ops.triton_kernel_wrap import kernel_side_table
kernel = kernel_side_table.get_kernel(self.kernel_idx)
configs = []
if isinstance(kernel, Autotuner):
configs = kernel.configs
kernel = kernel.fn
return kernel, configs
def codegen(self, wrapper):
kernel, configs = self.get_kernel_and_configs()
# Definition of kernel
new_name = wrapper.define_user_defined_triton_kernel(
kernel, configs, self.kwargs
)
args = self.codegen_kwargs()
if V.graph.cpp_wrapper:
# in C++ wrapper, we don't pass constexpr args, as they don't
# get added as parameters to the PTX code compiled from the
# user-defined Triton kernel (only non-constexpr args do)
args = [arg for i, arg in enumerate(args) if i not in kernel.constexprs]
# Call to kernel
self.codegen_comment(wrapper)
wrapper.generate_user_defined_triton_kernel(
new_name,
self.grid,
configs,
args,
)
def should_allocate(self):
return False
def has_side_effects(self):
# UserDefinedTritonKernel does not return anything, but rather
# modifies input in place, do not let it get DCEd
return True
def get_unbacked_symbol_defs(self):
return {}
def get_mutation_names(self):
return []
def __init__(self, *, kernel_idx, grid, kernel_args):
inputs = []
kwargs = dict()
constant_args = []
for k, v in kernel_args.items():
if isinstance(v, TensorBox):
t = InputsKernel.unwrap_storage_for_input(self.realize_input(v))
inputs.append(t)
kwargs[k] = t
else:
constant_args.append(v)
kwargs[k] = v
assert len(inputs) != 0
device = inputs[0].get_device()
super().__init__(
None,
NoneLayout(device), # type: ignore[arg-type]
inputs,
tuple(constant_args),
kwargs,
)
self.name = V.graph.register_buffer(self)
self.kernel_idx = kernel_idx
self.grid = grid
kernel, _ = self.get_kernel_and_configs()
# If we are autotuning, not all arguments will be passed
self.ordered_kwargs_for_cpp_kernel = [
arg for arg in kernel.arg_names if arg in kernel_args
]
mark_node_as_mutating(
self, *[a for a in kernel_args.values() if isinstance(a, TensorBox)]
)
def get_alias_names(self):
return [i.get_name() for i in self.inputs]
def mark_node_as_mutating(cur_buffer, *mutated_ops):
"""
Allows ops in mutated_ops to be marked as being mutated as well as
indicates to the scheduler that these ops depend on cur_buffer.
"""
for op in mutated_ops:
assert isinstance(op, TensorBox)
V.graph.mark_buffer_mutated(op.get_name())
MutationOutput(op.layout, op, cur_buffer)
class MutationOutput(ExternKernel):
def get_mutation_names(self):
return [self.inputs[0].get_name()]
def __init__(self, layout, input, parent):
super().__init__(None, layout, [input, parent], ())
self.name = V.graph.register_buffer(self)
def should_allocate(self):
return False
def is_no_op(self):
return True
def has_side_effects(self):
return True
def get_alias_names(self):
return [self.inputs[0].get_name()]
class InplaceBernoulliFallback(ExternKernel):
"""
This needs to be a custom class to handle mutation properly
"""
kernel = "aten.bernoulli_"
def codegen(self, wrapper):
(x,) = (t.codegen_reference() for t in self.inputs)
wrapper.writeline(
f"{self.kernel}({x}, {', '.join(map(repr, self.constant_args))})"
)
def should_allocate(self):
return False
def get_mutation_names(self):
return [self.inputs[0].get_name()]
def get_unbacked_symbol_defs(self):
return {}
def __init__(self, x, *constant_args):
super().__init__(
None,
NoneLayout(x.get_device()), # type: ignore[arg-type]
self.unwrap_storage([x]),
constant_args,
)
self.name = V.graph.register_buffer(self)
mark_node_as_mutating(self, x)
class AccumulateGrad(ExternKernel):
"""
This needs to be a custom class to handle mutation properly
"""
kernel = "inductor_ops.accumulate_grad_"
def codegen(self, wrapper):
(variable, new_grad) = (t.codegen_reference() for t in self.inputs)
wrapper.writeline(f"{self.kernel}({variable}, {new_grad})")
def should_allocate(self):
return False
def get_mutation_names(self):
return [self.inputs[0].get_name()]
def get_unbacked_symbol_defs(self):
return {}
def __init__(self, variable, new_grad):
super().__init__(
None,
NoneLayout(variable.get_device()), # type: ignore[arg-type]
self.unwrap_storage([variable, new_grad]),
)
self.name = V.graph.register_buffer(self)
mark_node_as_mutating(self, variable)
class ScatterFallback(ExternKernel):
"""
This needs to be a custom class to handle mutation properly.
This class handles both aten.scatter_ and aten.scatter_reduce_.
It also handle the case `src` being a scalar properly.
"""
def codegen(self, wrapper):
reduce = self.kwargs["reduce"]
if V.graph.cpp_wrapper:
# Follow aten/src/ATen/native/ReductionType.h:get_operator_enum
get_operator_enum = {"add": "sum", "multiply": "prod"}
if reduce in get_operator_enum:
reduce = get_operator_enum[reduce]
self.cpp_kernel = self.get_cpp_kernel(self.fn, reduce)
if self.src_is_tensor:
(x, index, src) = (t.codegen_reference() for t in self.inputs)
else:
(x, index) = (t.codegen_reference() for t in self.inputs)
src = self.constant_args[1]
wrapper.generate_scatter_fallback(
x,
[x, self.constant_args[0], index, src],
self.cpp_kernel if V.graph.cpp_wrapper else self.kernel,
self.fn,
self.src_is_tensor,
reduce,
self.codegen_kwargs(),
)
def should_allocate(self):
return False
def get_cpp_kernel(self, fn, reduce):
if fn == "aten.scatter_":
if self.src_is_tensor:
kernel = (
"at::scatter_out" if reduce is None else "at::scatter_reduce_out"
)
else:
assert (
reduce is None
), "Expect reduce to be None for aten.scatter_ with scalar src"
kernel = "at::scatter_out"
else:
assert (
reduce is not None
), "Expect reduce to be not None for aten.scatter_reduce_"
kernel = "at::scatter_reduce_out"
return kernel
def get_mutation_names(self):
return [self.inputs[0].get_name()]
def get_unbacked_symbol_defs(self):
return {}
def __init__(
self,
fn,
x,
dim: int,
index,
src,
*,
reduce: Optional[str] = None,
include_self: bool = True,
):
assert fn in {"aten.scatter_", "aten.scatter_reduce_"}
self.src_is_tensor = isinstance(src, TensorBox)
self.kernel = fn
self.fn = fn
constant_args: Tuple[Any, ...]
if self.src_is_tensor:
tensors = [self.realize_input(t) for t in [x, index, src]]
constant_args = (dim,)
else:
tensors = [self.realize_input(t) for t in [x, index]]
constant_args = (dim, src)
super().__init__(
None,
NoneLayout(x.get_device()), # type: ignore[arg-type]
self.unwrap_storage(tensors),
constant_args,
{"reduce": reduce, "include_self": include_self},
)
self.ordered_kwargs_for_cpp_kernel = ["reduce", "include_self"]
self.name = V.graph.register_buffer(self)
mark_node_as_mutating(self, x)
class IndexPutFallback(ExternKernel):
"""
This needs to be a custom class to handle mutation and indices properly
"""
def codegen(self, wrapper):
(x, values, *valid_indices) = (t.codegen_reference() for t in self.inputs)
indices = []
iter_valid_indices = iter(valid_indices)
for i, _ in enumerate(self.indices):
if self.indices[i] is not None:
indices.append(next(iter_valid_indices))
else:
indices.append(V.graph.wrapper_code.none_str)
indices_str = f"{V.graph.wrapper_code.open_bracket}{', '.join(indices)}{V.graph.wrapper_code.closed_bracket}"
args = [x, indices_str, values, *self.codegen_const_args()]
wrapper.writeline(
wrapper.wrap_kernel_call(
self.cpp_kernel if V.graph.cpp_wrapper else self.kernel, args
)
)
def should_allocate(self):
return False
def get_mutation_names(self):
return [self.inputs[0].get_name()]
def get_unbacked_symbol_defs(self):
return {}
def __init__(self, x, indices, values, accumulate):
self.indices = indices
valid_indices = [i for i in indices if i is not None]
tensors = [self.realize_input(x) for x in [x, values, *valid_indices]]
super().__init__(
None,
NoneLayout(x.get_device()), # type: ignore[arg-type]
self.unwrap_storage(tensors),
(accumulate,),
)
self.name = V.graph.register_buffer(self)
self.cpp_kernel = "at::index_put_"
self.kernel = "aten.index_put_"
mark_node_as_mutating(self, x)
class DeviceCopy(ExternKernelOut):
@classmethod
def create(cls, x, device):
if not x.is_extern() and all(
(r.name in V.graph.constants and isinstance(r, dependencies.MemoryDep))
for r in x.get_reads()
):
return x.constant_to_device(device)
V.graph.add_device_info(device)
V.graph.add_device_info(x.get_device())
developer_warning("DeviceCopy in input program")
return DeviceCopy(
FlexibleLayout(
device=device,
dtype=x.get_dtype(),
size=x.get_size(),
),
[cls.realize_input(x)],
)
def codegen(self, wrapper):
args = self.codegen_args()
assert len(args) == 1
if self.output_view:
wrapper.codegen_device_copy(args[0], self.output_view.codegen_reference())
else:
wrapper.codegen_device_copy(args[0], self.codegen_reference())
class DynamicScalar(ExternKernel):
"""
The result of a call to aten._local_scalar_dense.
"""
def get_reads(self):
return ()
def should_allocate(self):
return False
# TODO: handle bools carefully
def __init__(self, sym, data):
super().__init__(None, NoneLayout(torch.device("cpu")), [data]) # type: ignore[arg-type]
if isinstance(sym, sympy.Symbol):
self.sym = sym
self.is_bool = False
else:
# Special case for boolean. For Reasons(TM), we don't represent
# boolean variables directly in sympy; instead, we generate an
# indicator integer variable which we then convert to a boolean by
# testing i0 == 1. We have to identify the underlying indicator
# variable, and then bind i0 to the appropriate integer value
# based on the runtime boolean.
assert isinstance(sym, sympy.Eq), sym
assert isinstance(sym.args[0], sympy.Symbol), sym
assert sym.args[1] == 1, sym
self.sym = sym.args[0]
self.is_bool = True
def get_unbacked_symbol_defs(self):
return {self.sym}
def codegen(self, wrapper):
wrapper.codegen_dynamic_scalar(self)
@dataclasses.dataclass
class ExternKernelNode:
name: str
node: export_schema.Node
has_c_shim = {
aten._scaled_dot_product_flash_attention.default,
aten.addmm.out,
aten.bmm.out,
aten.mm.out,
aten._scaled_mm.default,
aten.repeat_interleave.Tensor,
aten.nonzero.default,
}
class FallbackKernel(ExternKernelAlloc):
args_default_value: List[Dict[str, Any]]
def __init__(
self,
layout,
kernel,
tensor_args,
nontensor_args,
unflatten_args,
kwargs=None,
):
super().__init__(
layout,
tuple(tensor_args),
tuple(nontensor_args),
)
# We need output buffers for generating kernel arguments in the
# abi-compatible mode, where we retrieve outputs by pass each individual
# output through the abi-compatible interface.
self.outputs: Sequence[Any] = []
self.use_runtime_dispatch = False
self.abi_compatible_kernel = None
assert isinstance(
kernel,
(
torch._ops.OpOverload,
torch._ops.HigherOrderOperator,
),
), f"Fails to create FallbackKernel for {kernel}: {type(kernel)} not supported"
self.op_overload = kernel
self.unflatten_args = unflatten_args
self.kwargs = {} if kwargs is None else kwargs
V.graph.warn_fallback(self.kernel)
def set_cpp_kernel(self, kernel):
from .codegen.wrapper import get_cpp_op_schema
assert (
not kernel._schema.is_mutable
), f"mutable {kernel.__name__} is not supported with cpp_wrapper"
# These checks are here because ops that return aliasing tensors will
# return type Tensor& instead of Tensor, but codegen will always write
# type Tensor on the LHS.
def is_not_write(arg):
return arg.alias_info is None or not arg.alias_info.is_write
assert all(
is_not_write(x) for x in kernel._schema.arguments
), f"{kernel.__name__} with alias_info arguments is not supported with cpp_wrapper"
assert all(
is_not_write(x) for x in kernel._schema.returns
), f"{kernel.__name__} with alias_info returns is not supported with cpp_wrapper"
self.cpp_kernel = kernel._schema.name
self.cpp_kernel_overload_name = kernel._schema.overload_name
self.cpp_kernel_key = (
f"{self.cpp_kernel.replace('::', '_')}_{self.cpp_kernel_overload_name}"
)
self.cpp_op_schema = get_cpp_op_schema(kernel)
self.ordered_kwargs_for_cpp_kernel = [
x.name for x in kernel._schema.arguments if x.kwarg_only
]
def is_legacy_abi_kernel(self):
return "_scaled_dot_product_flash_attention" in str(self.kernel)
def get_arg_default_value(self, pos):
assert hasattr(
self, "args_default_value"
), "self.args_default_value has to be provided"
assert pos < len(
self.args_default_value
), f"expected the index {pos} to be smaller than len(self.args_default_value): {len(self.args_default_value)}"
return self.args_default_value[pos]["value"]
# Generate abi-compatible kernel names for shim kernels.
# Each individual shim kernel may have its own versioning rule.
# However, we don't expect we would end up with too many of such rules.
def _get_abi_compatible_kernel(self):
if not V.graph.cpp_wrapper:
return self.kernel
def sdpa_ver_fn():
# For sdpa, we need the v2 version only if any optional
# kwarg is missing.
if any(
self.get_kwargs_value(arg_name) is None
for arg_name in self.ordered_kwargs_for_cpp_kernel
):
return f"{self.cpp_kernel}_v2"
else:
return self.cpp_kernel
kernel_to_ver = {"at::_scaled_dot_product_flash_attention": sdpa_ver_fn}
if (ver_fn := kernel_to_ver.get(self.cpp_kernel, None)) is not None:
return ver_fn()
return self.cpp_kernel
def codegen_args(self):
@dataclasses.dataclass
class Shim:
ref: Any
def __repr__(self):
return self.ref
tensor_args = [Shim(x.codegen_reference()) for x in self.inputs]
args, kwargs = self.unflatten_args(tensor_args, self.constant_args)
# Now we setup abi_compatible_kernel after self.kernel
# and kwargs are adjusted appropriately.
self.abi_compatible_kernel = self._get_abi_compatible_kernel()
if V.graph.cpp_wrapper and isinstance(self.op_overload, torch._ops.OpOverload):
args = [
V.graph.wrapper_code.val_to_cpp_arg_str(
param.real_type, x, self.is_legacy_abi_kernel()
)
for param, x in zip(self.op_overload._schema.arguments, args)
]
else:
args = [V.graph.wrapper_code.val_to_arg_str(x) for x in args]
# Previously, we want to maintain forward-compatibility by skipping
# default args in the serialized artifacts in fbcode. However,
# some of our shim interfaces require default values being set.
# Discussed with Sherlock offline and we decided to allow serializing
# default args into the C++ wrapper code for now. We will refine this
# part if we see real FC requirement. More details related to FC
# can be found at:
# https://docs.google.com/document/d/1FzWm-sHYwmRi3x_g036kOxd99KaYquUsA-L5JwOn8ys/edit?usp=sharing
if V.graph.cpp_wrapper and hasattr(self, "args_default_value"):
n_args = len(args)
n_pos_args = len(self.args_default_value)
# Some positional args are not provided, need to use their default value in cpp wrapper
if n_args < n_pos_args:
pos_args = [
self.get_arg_default_value(i) for i in range(n_args, n_pos_args)
]
pos_args = [V.graph.wrapper_code.val_to_arg_str(x) for x in pos_args]
args.extend(pos_args)
# let self.codegen_kwargs handle kwargs
self.kwargs.update(kwargs)
return args
@staticmethod
def find_device(tensor_args, example_output):
if tensor_args:
return tensor_args[0].get_device()
if isinstance(example_output, torch.Tensor):
return example_output.device
if isinstance(example_output, (list, tuple)):
devices = {FallbackKernel.find_device(None, x) for x in example_output}
# Remove None
devices = [device for device in devices if device]
if len(devices) == 1:
return devices[0]
for device in devices:
if device.type == "cuda":
return device
return devices[0]
return None
def has_side_effects(self):
# TODO - some fallbacks are still OpOverloadPackets
if not isinstance(self.op_overload, torch._ops.OpOverload):
return False
return get_schema_info(self.op_overload).is_mutable()
def get_alias_names(self):
# TODO - some fallbacks are still OpOverloadPackets
if not isinstance(self.op_overload, torch._ops.OpOverload):
return []
if torch._inductor.utils.is_view(self.op_overload):
# TODO - use op._schema.arguments alias_info to figure out
# precise list
return [inp.get_name() for inp in self.inputs]
return []
# ProxyExecutor Design Note
# We export the ExternFallbackNodes (for custom ops) into a serialized file
# and run it with a host side proxy executor to address the ABI problem
# This is currently only implemented for fbcode. Eventually, we will also make this work for OSS.
# Detailed design doc can be found at
# https://docs.google.com/document/d/1wC4DOZFaYym2t1Esz0X5yxlLI3RDnSiyRbUus3bkJ64/edit?usp=sharing
def export_extern_kernel_node(self):
assert isinstance(self, FallbackKernel)
args, kwargs = self.unflatten_args(self.inputs, self.constant_args)
ordered_kwargs = [
kwargs.get(key, None) for key in self.ordered_kwargs_for_cpp_kernel
]
serializer = GraphModuleSerializer(None, None) # type: ignore[arg-type]
named_arguments = serializer.serialize_inputs(self.op_overload, args, kwargs) # type: ignore[arg-type]
# serialize_outputs
def handle_single_output(return_type, output):
if isinstance(return_type, torch.TensorType):
# For single Tensor
out = output
if isinstance(output, (list, tuple)):
assert len(output) == 1
out = output[0]
return export_schema.Argument.create(
as_tensor=export_schema.TensorArgument(name=out.get_name())
)
elif isinstance(return_type, torch.ListType) and isinstance(
return_type.getElementType(), torch.TensorType
):
# For single TensorList
return export_schema.Argument.create(
as_tensors=[
export_schema.TensorArgument(name=out.get_name())
for out in output
]
)
else:
raise RuntimeError(f"Unsupported return type {type(return_type)}")
target = self.op_overload
returns = target._schema.returns # type: ignore[union-attr]
if len(returns) == 1:
return_type = returns[0].real_type
output_arguments = [handle_single_output(return_type, self.outputs)]
else:
# For tuple returns, e.g "-> (Tensor, Tensor)" or "-> (Tesnor, Tensor[])"
assert isinstance(self.outputs, tuple)
assert len(returns) == len(self.outputs)
output_arguments = [
handle_single_output(return_schema.real_type, output)
for return_schema, output in zip(returns, self.outputs)
]
node = ExternKernelNode(
name=self.get_name(),
node=export_schema.Node(
target=self.op_overload.name(),
inputs=named_arguments,
outputs=output_arguments,
metadata={},
),
)
V.graph.extern_kernel_nodes.append(node)
return [*args, *ordered_kwargs]
def codegen(self, wrapper):
kernel = self.op_overload
if kernel.namespace == "aten":
# Aten Fallback Ops
assert isinstance(kernel, torch._ops.OpOverload)
op_base_name = kernel.__name__.split(".")[0]
if V.graph.cpp_wrapper:
if config.is_fbcode() and kernel not in has_c_shim:
log.warning(
"%s is missing a c-shim implementation, using proxy executor as fallback",
kernel,
)
self.use_runtime_dispatch = True
self.set_cpp_kernel(kernel)
else:
# Calling with the default kernel name can lead to ambiguous behavior like the following example.
# repeat_interleave(const at::Tensor & repeats, c10::optional<int64_t> output_size=c10::nullopt)
# repeat_interleave(const at::Tensor & self, int64_t repeats,
# c10::optional<int64_t> dim=c10::nullopt, c10::optional<int64_t> output_size=c10::nullopt)
self.cpp_kernel = (
f"at::{op_base_name}"
if kernel._overloadname == "default"
else f"at::_ops::{kernel.__name__.replace('.', '_')}::call"
)
schema = kernel._schema
self.args_default_value = [
{"type": x.real_type, "value": x.default_value}
for x in schema.arguments
if not x.kwarg_only
]
self.ordered_kwargs_for_cpp_kernel = [
x.name for x in schema.arguments if x.kwarg_only
]
self.kwargs_default_value = {
x.name: {"type": x.real_type, "value": x.default_value}
for x in schema.arguments
if x.kwarg_only
}
else:
self.kernel = f"aten.{op_base_name}"
elif isinstance(kernel, torch._ops.HigherOrderOperator):
if getattr(torch._prims.rng_prims, kernel.__name__, None) is kernel:
self.kernel = f"torch._prims.rng_prims.{kernel.__name__}"
else:
raise NotImplementedError(
"Unable to find HigherOrderOperator kernel name"
)
else:
# For non-aten OpOverload, i.e. custom ops
if V.graph.cpp_wrapper:
self.use_runtime_dispatch = True
self.set_cpp_kernel(kernel)
else:
self.kernel = (
f"{kernel.__module__.replace('._ops.', '.ops.')}.{kernel.__name__}"
)
if self.use_runtime_dispatch:
self.codegen_comment(wrapper)
exported_args = None
args = None
if config.is_fbcode() and V.graph.cpp_wrapper:
exported_args = self.export_extern_kernel_node()
else:
args = [*self.codegen_args(), *self.codegen_kwargs()]
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.codegen_kernel_name(),
args,
self.cpp_op_schema,
self.cpp_kernel_key,
self.cpp_kernel_overload_name,
self.op_overload,
exported_args,
self.outputs,
)
else:
self.codegen_comment(wrapper)
args = [*self.codegen_args(), *self.codegen_kwargs()]
V.graph.wrapper_code.generate_fallback_kernel(self, args)
if isinstance(self.layout, Layout):
self.codegen_size_asserts(wrapper)
@staticmethod
def tensor_to_layout(output: torch.Tensor):
return FixedLayout(
output.device,
output.dtype,
convert_shape_to_inductor(output.size()),
convert_shape_to_inductor(output.stride()),
)
@classmethod
def create(cls, kernel, *args, **kwargs):
fake_incorrect_kernels = (aten._fused_moving_avg_obs_fq_helper_functional,)
context = (
V.graph.fake_mode if kernel not in fake_incorrect_kernels else nullcontext()
)
with context:
(
example_output,
tensor_args,
non_tensor_args,
unflatten_args,
) = cls.process_kernel(kernel, *args, **kwargs)
device = cls.find_device(tensor_args, example_output)
assert device, "Not sure where to find device info"
packed = cls(
MultiOutputLayout(device),
kernel,
tensor_args,
non_tensor_args,
unflatten_args,
)
def generate_output(output, indices):
if isinstance(output, (list, tuple)):
return type(output)(
generate_output(output[i], indices + [(type(output), i)])
for i in range(len(output))
)
elif isinstance(output, dict):
return {
key: generate_output(val, indices + [(type(output), key)])
for key, val in output.items()
}
elif isinstance(output, torch.Tensor):
return MultiOutput(
cls.tensor_to_layout(output),
packed,
indices,
)
elif isinstance(output, int):
return output
elif isinstance(output, torch.SymInt):
return output.node.expr
else:
assert (
output is None
), f"FallbackKernel output type {type(output)} is not supported"
return None
outputs = generate_output(example_output, [])
if isinstance(outputs, (list, tuple, dict)):
packed.outputs = outputs # type: ignore[assignment]
else:
packed.outputs = [outputs]
return outputs
def apply_constraint(self):
return super().apply_constraint()
@dataclasses.dataclass
class ComplexView(ExternKernelAlloc):
"""View a complex number as two dtyped numbers or vice versa"""
def should_allocate(self):
return False
def get_alias_names(self):
# Signal to codegen that our output buffer isn't safe to reuse
return [self.inputs[0].get_name()]
def __init__(
self,
layout,
kernel,
tensor_args,
nontensor_args,
):
super().__init__(
layout,
tuple(tensor_args),
tuple(nontensor_args),
)
# We need output buffers for generating kernel arguments in the
# abi-compatible mode, where we retrieve outputs by pass each individual
# output through the abi-compatible interface.
self.outputs: Sequence[Any] = []
self.kernel = kernel
@classmethod
def create(cls, kernel, *args, **kwargs):
context = V.graph.fake_mode
with context:
(
example_output,
tensor_args,
non_tensor_args,
unflatten_args,
) = cls.process_kernel(kernel, *args, **kwargs)
device = FallbackKernel.find_device(tensor_args, example_output)
assert device, "Not sure where to find device info"
packed = ComplexView(
MultiOutputLayout(device), kernel, tensor_args, non_tensor_args
)
layout = FixedLayout(
example_output.device,
example_output.dtype,
convert_shape_to_inductor(example_output.size()),
convert_shape_to_inductor(example_output.stride()),
)
outputs = MultiOutput(layout, packed, [])
packed.outputs = [outputs]
return outputs
@dataclasses.dataclass
class MultiOutputLayout(IRNode):
device: torch.device
class MultiOutput(ExternKernel):
# Given an input MultiOutputLayout buffer, indexes out an actual buffer
# from that result. This doesn't actually produce multiple outputs,
# that's MultiOutputLayout!
def codegen_list_tuple_access(self, basename, indices):
if len(indices) > 0:
itype, i = indices[0]
if itype == list:
return self.codegen_list_tuple_access(f"{basename}[{i}]", indices[1:])
elif itype == tuple:
# cpp wrapper code needs to use std::get<> to access a tuple
tuple_access = V.graph.wrapper_code.codegen_tuple_access(
basename, self.get_name(), str(i)
)
return self.codegen_list_tuple_access(tuple_access, indices[1:])
elif itype == dict:
return self.codegen_list_tuple_access(f"{basename}['{i}']", indices[1:])
else:
raise AssertionError("non supported index type")
else:
return basename
def codegen(self, wrapper):
wrapper.codegen_multi_output(
self.get_name(),
self.codegen_list_tuple_access(self.inputs[0].get_name(), self.indices),
)
self.codegen_unbacked_symbol_defs(wrapper)
def __init__(self, layout, input, indices: List[Tuple[Any, ...]]):
super().__init__(None, layout, [input], ())
self.name = V.graph.register_buffer(self)
self.indices = indices
def get_unbacked_symbol_uses(self):
return self.inputs[0].get_unbacked_symbol_uses()
def should_allocate(self):
return False
def get_alias_names(self):
return [
inp.get_name()
for inp in self.inputs
if isinstance(inp, (FallbackKernel, ComplexView))
and len(inp.get_alias_names()) > 0
]
def _prepare_convolution_fusion_create(
cls,
x: "TensorBox",
weight: "TensorBox",
bias: "TensorBox",
padding: List[int],
stride: List[int],
dilation: List[int],
groups: int,
transposed: bool = False,
output_padding: Optional[List[int]] = None,
):
"""
This function is a helper function to prepare inputs, layout and constant args
for convolution post-op fusion's create function, including deciding the output
layout (channels first or channels last), realizing inputs and make them etc. The
function only supports the CPU device since conv post-op fusion kernel is only
supported on CPU right now.
"""
# Port from aten/src/ATen/native/ConvUtils.h: _conv_input_size
def _conv_input_size(
output_size, weight_size, padding, output_padding, stride, dilation, groups
):
assert len(output_size) == len(weight_size), "Expect input dim == weight dim"
dim = len(output_size)
assert dim > 2, "Expect input dim > 2"
BATCH_DIM = 0
WEIGHT_INPUT_CHANNELS_DIM = 1
input_size = []
input_size.append(output_size[BATCH_DIM])
input_size.append(weight_size[WEIGHT_INPUT_CHANNELS_DIM] * groups)
for d in range(2, dim):
kernel = (weight_size[d] - 1) * dilation[d - 2] + 1
input_size_d = (
(output_size[d] - 1) * stride[d - 2]
- (padding[d - 2] * 2)
+ kernel
+ output_padding[d - 2]
)
input_size.append(input_size_d)
return list(map(int, input_size))
# The size of prepacked_weight is the prepacked weight size of deconv:
# Groups > 1: [g*o, i/g, ...]
# Groups == 1: [o, i, ...]
# Returns original weight size in [i, o, ...]
def _original_deconv_weight_size(
prepacked_weight,
groups,
):
prepacked_weight_size = prepacked_weight.size()
dim = len(prepacked_weight_size)
assert dim > 2, "Expect weight dim > 2"
if groups > 1:
weight_size = []
weight_size.append(prepacked_weight_size[1] * groups)
weight_size.append(prepacked_weight_size[0] / groups)
for d in range(2, dim):
weight_size.append(prepacked_weight_size[d])
else:
weight_size = prepacked_weight.transpose(0, 1).size()
return weight_size
x.realize()
weight.realize()
if bias is not None:
bias.realize()
with V.graph.fake_mode:
# TODO <Leslie> cleaned up the fake_tensor trace as Linear implementation
x_fake = ir_node_to_tensor(x, guard_shape=True)
weight_fake = ir_node_to_tensor(weight, guard_shape=True)
dims = len(x_fake.size()) - 2
assert 0 < len(padding) <= dims
assert 0 < len(dilation) <= dims
assert 0 < len(stride) <= dims
padding = pad_listlike(padding, dims)
dilation = pad_listlike(dilation, dims)
stride = pad_listlike(stride, dims)
if output_padding is None:
output_padding = pad_listlike([0], dims)
else:
assert 0 < len(output_padding) <= dims
output_padding = pad_listlike(output_padding, dims)
assert isinstance(groups, int)
if transposed:
# When transposed, the size of the prepacked oneDNN weight is different
# from the PyTorch weight. We're not able to run aten conv with such
# size. We infer the output size from the input params here:
weight_size = _original_deconv_weight_size(weight_fake, groups)
input_size = x_fake.size()
output_size = _conv_input_size(
input_size,
weight_size,
padding,
output_padding,
stride,
dilation,
groups,
)
else:
bias_fake = (
ir_node_to_tensor(bias, guard_shape=True) if bias is not None else bias
)
output = torch.ops.aten.convolution(
x_fake,
weight_fake,
bias_fake,
stride,
padding,
dilation,
transposed,
output_padding,
groups,
)
output_size = output.size()
req_stride_order = [0] + list(reversed(range(1, len(stride) + 1)))
req_stride_order = [len(req_stride_order)] + req_stride_order
output_stride = make_channels_last_strides_for(output_size)
x = cls.require_stride_order(x, req_stride_order)
assert x.get_device().type == "cpu" and weight.get_device().type == "cpu"
inputs = [x, weight]
kernel_layout = FixedLayout(
x.get_device(),
x.get_dtype(),
convert_shape_to_inductor(output_size),
convert_shape_to_inductor(output_stride),
)
constant_args = [padding, stride, dilation, groups]
if transposed:
constant_args.insert(1, output_padding)
if bias is not None:
inputs.append(bias)
else:
constant_args.insert(0, bias)
return inputs, constant_args, kernel_layout, req_stride_order
def _prepare_linear_fusion_create(
cls,
x: "TensorBox",
weight: "TensorBox",
bias: "TensorBox",
):
"""
This function is a helper function to prepare inputs, layout and constant args
for linear post-op fusion's create function. The function only supports the CPU device
since linear post-op fusion kernel is only supported on CPU right now.
"""
x.realize()
weight.realize()
if bias is not None:
bias.realize()
*m, _ = x.get_size()
# The weight has been transposed during the qlinear weight prepack process.
# https://github.com/pytorch/pytorch/blob/4979f9c0d72490970e2019bb1d2284f83d93f76b/
# aten/src/ATen/native/quantized/cpu/qlinear_prepack.cpp#L291
_, oc = weight.get_size()
output_size = list(m) + [oc]
req_stride_order = list(reversed(range(len(x.get_size()))))
x = cls.require_stride_order(x, req_stride_order)
assert x.get_device().type == "cpu" and weight.get_device().type == "cpu"
inputs = [x, weight]
output_stride = make_contiguous_strides_for(output_size)
kernel_layout = FixedLayout(
x.get_device(),
x.get_dtype(),
output_size,
output_stride,
)
constant_args: List[Any] = []
if bias is not None:
inputs.append(bias)
else:
constant_args.insert(0, bias)
return inputs, constant_args, kernel_layout, req_stride_order
class ConvolutionUnary(ExternKernelAlloc):
def __init__(
self,
layout,
inputs,
constant_args=(),
kernel="torch.ops.mkldnn._convolution_pointwise",
):
super().__init__(
layout,
inputs,
constant_args,
None,
kernel="torch.ops.mkldnn._convolution_pointwise",
cpp_kernel="mkldnn::_convolution_pointwise",
)
self.cpp_kernel_key = "convolution_pointwise"
self.cpp_op_schema = """
at::Tensor(
const at::Tensor& input_t,
const at::Tensor& weight_t,
const c10::optional<at::Tensor>& bias_opt,
at::IntArrayRef padding,
at::IntArrayRef stride,
at::IntArrayRef dilation,
int64_t groups,
c10::string_view attr,
torch::List<c10::optional<at::Scalar>> scalars,
c10::optional<c10::string_view> algorithm)"""
def codegen(self, wrapper):
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.cpp_kernel if V.graph.cpp_wrapper else self.kernel,
self.codegen_args(),
self.cpp_op_schema,
self.cpp_kernel_key,
)
if isinstance(self.layout, Layout):
self.codegen_size_asserts(wrapper)
@classmethod
def create(
cls,
x: "TensorBox",
weight: "TensorBox",
bias: "TensorBox",
padding_: List[int],
stride_: List[int],
dilation_: List[int],
groups: int,
attr,
scalars: Optional[List[Any]],
algorithm,
):
(inputs, constant_args, kernel_layout, _) = _prepare_convolution_fusion_create(
cls, x, weight, bias, padding_, stride_, dilation_, groups
)
constant_args = constant_args + [
attr,
may_convert_to_optional(scalars),
algorithm,
]
return ConvolutionUnary(
layout=kernel_layout,
inputs=inputs,
constant_args=constant_args,
)
class ConvolutionBinary(ExternKernelAlloc):
def __init__(
self,
layout,
inputs,
constant_args=(),
cpp_constant_args=(),
):
super().__init__(
layout,
inputs,
constant_args,
None,
kernel="torch.ops.mkldnn._convolution_pointwise.binary",
cpp_kernel="mkldnn::_convolution_pointwise",
)
self.cpp_kernel_overload_name = "binary"
self.cpp_kernel_key = "convolution_pointwise_binary"
self.cpp_op_schema = """
at::Tensor(
const at::Tensor& input_t,
const at::Tensor& other_t,
const at::Tensor& weight_t,
const c10::optional<at::Tensor>& bias_opt,
at::IntArrayRef padding,
at::IntArrayRef stride,
at::IntArrayRef dilation,
int64_t groups,
c10::string_view binary_attr,
c10::optional<at::Scalar> alpha,
c10::optional<c10::string_view> unary_attr,
torch::List<c10::optional<at::Scalar>> unary_scalars,
c10::optional<c10::string_view> unary_algorithm)"""
self.cpp_constant_args = cpp_constant_args
def codegen(self, wrapper):
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.codegen_kernel_name(),
self.codegen_args(),
self.cpp_op_schema,
self.cpp_kernel_key,
self.cpp_kernel_overload_name,
)
if isinstance(self.layout, Layout):
self.codegen_size_asserts(wrapper)
@classmethod
def create(
cls,
x: "TensorBox",
other: "TensorBox",
weight: "TensorBox",
bias: "TensorBox",
padding_: List[int],
stride_: List[int],
dilation_: List[int],
groups: int,
binary_attr: str,
binary_alpha: Optional[float],
unary_attr: Optional[str],
unary_scalars: Optional[List[Any]],
unary_algorithm: Optional[str],
):
(
inputs,
constant_args,
kernel_layout,
req_stride_order,
) = _prepare_convolution_fusion_create(
cls, x, weight, bias, padding_, stride_, dilation_, groups
)
other = cls.require_stride_order(other, req_stride_order)
inputs.insert(1, other)
constant_args = constant_args + [
binary_attr,
binary_alpha,
unary_attr,
may_convert_to_optional(unary_scalars),
unary_algorithm,
]
return ConvolutionBinary(
layout=kernel_layout,
inputs=inputs,
constant_args=constant_args,
)
class ConvolutionBinaryInplace(ExternKernelAlloc):
def __init__(
self,
kernel_layout,
inputs,
constant_args=(),
):
# Due to constrain of op.call, other (Tensor&) should be at input[0]
reordered_inputs = [inputs[1], inputs[0]] + inputs[2:]
super().__init__(
kernel_layout,
reordered_inputs,
constant_args,
None,
kernel="torch.ops.mkldnn._convolution_pointwise_.binary",
cpp_kernel="mkldnn::_convolution_pointwise_",
)
self.cpp_kernel_overload_name = "binary"
self.cpp_kernel_key = "convolution_pointwise_binary_"
# TODO: op.call: input[0] should be at::Tensor&
self.cpp_op_schema = """
at::Tensor&(
at::Tensor& other_t,
const at::Tensor& input_t,
const at::Tensor& weight_t,
const c10::optional<at::Tensor>& bias_opt,
at::IntArrayRef padding,
at::IntArrayRef stride,
at::IntArrayRef dilation,
int64_t groups,
c10::string_view binary_attr,
c10::optional<at::Scalar> alpha,
c10::optional<c10::string_view> unary_attr,
torch::List<c10::optional<at::Scalar>> unary_scalars,
c10::optional<c10::string_view> unary_algorithm)"""
def codegen(self, wrapper):
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.codegen_kernel_name(),
self.codegen_args(),
self.cpp_op_schema,
self.cpp_kernel_key,
self.cpp_kernel_overload_name,
)
def get_mutation_names(self):
return [self.inputs[0].get_name()]
def get_unbacked_symbol_defs(self):
return {}
@classmethod
def create(
cls,
x: "TensorBox",
other: "TensorBox",
weight: "TensorBox",
bias: "TensorBox",
padding_: List[int],
stride_: List[int],
dilation_: List[int],
groups: int,
binary_attr: str,
binary_alpha: Optional[float],
unary_attr: Optional[str],
unary_scalars: Optional[List[Any]],
unary_algorithm: Optional[str],
):
(
inputs,
constant_args,
_,
req_stride_order,
) = _prepare_convolution_fusion_create(
cls, x, weight, bias, padding_, stride_, dilation_, groups
)
other = cls.require_stride_order(other, req_stride_order)
inputs.insert(1, other)
constant_args = constant_args + [
binary_attr,
binary_alpha,
unary_attr,
may_convert_to_optional(unary_scalars),
unary_algorithm,
]
packed = ConvolutionBinaryInplace(
kernel_layout=NoneLayout(inputs[1].get_device()), # type: ignore[arg-type]
inputs=inputs,
constant_args=constant_args,
)
mark_node_as_mutating(packed, inputs[1])
# This op mutates in place which means that the result is not the
# target but rather the input that is being mutated
# init reorders the inputs, so inputs[1] becomes packed.inputs[0]
return packed.inputs[0]
class MKLPackedLinear(ExternKernelAlloc):
def __init__(
self,
layout,
inputs,
constant_args=(),
):
super().__init__(
layout,
inputs,
constant_args,
None,
kernel="torch.ops.mkl._mkl_linear",
cpp_kernel="mkl::_mkl_linear",
)
self.cpp_kernel_key = "mkl_linear"
self.cpp_op_schema = """
at::Tensor(
const at::Tensor& self,
const at::Tensor& mkl_weight_t,
const at::Tensor& origin_weight_t,
const c10::optional<at::Tensor>& bias_opt,
const int64_t prepack_batch_size)"""
def codegen(self, wrapper):
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.codegen_kernel_name(),
self.codegen_args(),
self.cpp_op_schema,
self.cpp_kernel_key,
)
@classmethod
def create(cls, x, packed_w, orig_w, batch_size):
x = cls.require_stride1(cls.realize_input(x))
orig_w = cls.require_stride1(cls.realize_input(orig_w))
*m, _ = x.get_size()
oc, _ = orig_w.get_size()
output_size = list(m) + [oc]
output_stride = make_contiguous_strides_for(output_size)
inputs = [x, packed_w, orig_w]
constant_args = [None, batch_size]
return MKLPackedLinear(
layout=FixedLayout(
x.get_device(), x.get_dtype(), output_size, output_stride
),
inputs=inputs,
constant_args=constant_args,
)
class LinearUnary(ExternKernelAlloc):
def __init__(
self,
layout,
inputs,
constant_args=(),
):
super().__init__(
layout,
inputs,
constant_args,
None,
kernel="torch.ops.mkldnn._linear_pointwise",
cpp_kernel="mkldnn::_linear_pointwise",
)
self.cpp_kernel_key = "linear_pointwise"
self.cpp_op_schema = """
at::Tensor(
const at::Tensor& input_t,
const at::Tensor& weight_t,
const c10::optional<at::Tensor>& bias_opt,
c10::string_view attr,
torch::List<c10::optional<at::Scalar>> scalars,
c10::optional<c10::string_view> algorithm)"""
def codegen(self, wrapper):
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.codegen_kernel_name(),
self.codegen_args(),
self.cpp_op_schema,
self.cpp_kernel_key,
)
@classmethod
def create(cls, x, w, b, attr, scalars, algorithm):
x = cls.require_contiguous(cls.realize_input(x))
w = cls.require_contiguous(cls.realize_input(w))
*m, ic = x.get_size()
oc, ic = w.get_size()
inputs = [x, w]
constant_args = [attr, scalars if scalars else [-1], algorithm]
if b is not None:
b = cls.require_contiguous(cls.realize_input(b))
inputs.append(b)
else:
constant_args.insert(0, None)
return LinearUnary(
layout=FlexibleLayout(
device=x.get_device(),
dtype=x.get_dtype(),
size=list(m) + [oc],
),
inputs=inputs,
constant_args=constant_args,
)
def apply_constraint(self):
pass
class LinearBinary(ExternKernelAlloc):
kernel = "torch.ops.mkldnn._linear_pointwise.binary"
def __init__(
self,
layout,
inputs,
constant_args=(),
):
super().__init__(
layout,
inputs,
constant_args,
None,
kernel="torch.ops.mkldnn._linear_pointwise.binary",
cpp_kernel="mkldnn::_linear_pointwise",
)
self.cpp_kernel_overload_name = "binary"
self.cpp_kernel_key = "linear_pointwise_binary"
self.cpp_op_schema = """
at::Tensor(
const at::Tensor& input_t,
const at::Tensor& other_t,
const at::Tensor& weight_t,
const c10::optional<at::Tensor>& bias_opt,
c10::string_view attr)
"""
def codegen(self, wrapper):
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.codegen_kernel_name(),
self.codegen_args(),
self.cpp_op_schema,
self.cpp_kernel_key,
self.cpp_kernel_overload_name,
)
@classmethod
def create(cls, x, y, w, b, attr):
x = cls.require_contiguous(cls.realize_input(x))
y = cls.require_contiguous(cls.realize_input(y))
w = cls.require_contiguous(cls.realize_input(w))
*m, ic = x.get_size()
oc, ic = w.get_size()
inputs = [x, y, w]
constant_args = [attr]
if b is not None:
b = cls.require_contiguous(cls.realize_input(b))
inputs.append(b)
else:
constant_args.insert(0, b)
return LinearBinary(
layout=FlexibleLayout(
device=x.get_device(),
dtype=x.get_dtype(),
size=list(m) + [oc],
),
inputs=inputs,
constant_args=constant_args,
)
def apply_constraint(self):
pass
class ConvolutionTransposeUnary(ExternKernelAlloc):
def __init__(
self,
layout,
inputs,
constant_args=(),
):
super().__init__(
layout,
inputs,
constant_args,
None,
kernel="torch.ops.mkldnn._convolution_transpose_pointwise",
cpp_kernel="mkldnn::_convolution_transpose_pointwise",
)
self.cpp_kernel_key = "convolution_transpose_pointwise"
self.cpp_op_schema = """
at::Tensor(
const at::Tensor& input_t,
const at::Tensor& weight_t,
const c10::optional<at::Tensor>& bias_opt,
at::IntArrayRef padding,
at::IntArrayRef output_padding,
at::IntArrayRef stride,
at::IntArrayRef dilation,
int64_t groups,
c10::string_view attr,
torch::List<c10::optional<at::Scalar>> scalars,
c10::optional<c10::string_view> algorithm)"""
def codegen(self, wrapper):
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.codegen_kernel_name(),
self.codegen_args(),
self.cpp_op_schema,
self.cpp_kernel_key,
)
@classmethod
def create(
cls,
x: "TensorBox",
weight: "TensorBox",
bias: "TensorBox",
padding_: List[int],
output_padding_: List[int],
stride_: List[int],
dilation_: List[int],
groups_: int,
attr,
scalars: Optional[List[Any]],
algorithm,
):
transposed = True
(
inputs,
constant_args,
kernel_layout,
_,
) = _prepare_convolution_fusion_create(
cls,
x,
weight,
bias,
padding_,
stride_,
dilation_,
groups_,
transposed,
output_padding_,
)
constant_args = constant_args + [
attr,
may_convert_to_optional(scalars),
algorithm,
]
return ConvolutionTransposeUnary(
layout=kernel_layout,
inputs=inputs,
constant_args=constant_args,
)
class MkldnnRnnLayer(ExternKernelAlloc):
def __init__(
self,
layout,
inputs,
constant_args=(),
):
super().__init__(
layout,
inputs,
constant_args,
None,
kernel="aten.mkldnn_rnn_layer",
cpp_kernel="at::mkldnn_rnn_layer",
)
@classmethod
def create(
cls,
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,
):
x = cls.require_stride1(cls.realize_input(x))
# If batch_first, x has been permuted in lstm before entering the mkldnn_rnn_layer.
# Make sure x is contiguous in batch_first case.
x.freeze_layout()
w0 = cls.require_stride1(cls.realize_input(w0))
w1 = cls.require_stride1(cls.realize_input(w1))
w2 = cls.require_stride1(cls.realize_input(w2))
w3 = cls.require_stride1(cls.realize_input(w3))
hx = cls.require_stride1(cls.realize_input(hx))
hx.freeze_layout()
cx = cls.require_stride1(cls.realize_input(cx))
cx.freeze_layout()
input_size = x.get_size()
assert len(input_size) == 3, "Expect lstm input to be 3D"
# batch_first is handled in the lstm OP. When entering
# rnn_layer here, we'll always have batch_first = False
seq_length, mini_batch, input_size = input_size
output_shape = [seq_length, mini_batch, hidden_size]
hy_shape = hx.get_size()
cy_shape = cx.get_size()
res: List[IRNode] = []
inputs = [x, w0, w1, w2, w3, hx, cx]
constant_args = [
reverse,
batch_sizes,
mode,
hidden_size,
num_layers,
has_biases,
bidirectional,
batch_first,
train,
]
packed = MkldnnRnnLayer(
MultiOutputLayout(x.get_device()),
inputs=inputs,
constant_args=constant_args,
)
def get_strides_of_lstm_output(output_shape, batch_first):
assert len(output_shape) == 3, "Expect output_shape to be 3D"
return make_contiguous_strides_for(output_shape)
output_sizes = [output_shape, hy_shape, cy_shape]
output_strides = [
get_strides_of_lstm_output(output_shape, batch_first),
make_contiguous_strides_for(hy_shape),
make_contiguous_strides_for(cy_shape),
]
output_ir = [
MultiOutput(
FixedLayout(
x.get_device(),
x.get_dtype(),
output_size,
output_stride,
),
packed,
[(tuple, i)],
)
for i, (output_size, output_stride) in enumerate(
zip(output_sizes, output_strides)
)
]
return output_ir
class QConvPointWisePT2E(ExternKernelAlloc):
def __init__(
self,
layout,
inputs,
constant_args=(),
):
"""
if bias is not None
- inputs = [x, w, b, weight_scale, weight_zp]
- const_args is: [stride, padding, dilation, groups, x_scale, x_zp, o_inv_scale, o_zp,
fp32_output, unary_attr, unary_scalars, unary_algorithm]
else
- inputs = [x, w, weight_scale, weight_zp]
- const_args is: [bias, stride, padding, dilation, groups, x_scale, x_zp, o_inv_scale, o_zp,
fp32_output, unary_attr, unary_scalars, unary_algorithm]
"""
self.has_bias = len(inputs) == 5
super().__init__(
layout,
inputs,
constant_args,
None,
kernel="torch.ops.onednn.qconv2d_pointwise",
cpp_kernel="onednn::qconv2d_pointwise",
)
self.cpp_kernel_key = "qconv2d_pointwise"
self.cpp_op_schema = """
at::Tensor(
at::Tensor act,
double act_scale,
int64_t act_zero_point,
at::Tensor weight,
at::Tensor weight_scales,
at::Tensor weight_zero_points,
c10::optional<at::Tensor> bias,
torch::List<int64_t> stride,
torch::List<int64_t> padding,
torch::List<int64_t> dilation,
int64_t groups,
double inv_output_scale,
int64_t output_zero_point,
c10::optional<c10::ScalarType> output_dtype,
c10::string_view attr,
torch::List<c10::optional<at::Scalar>> scalars,
c10::optional<c10::string_view> algorithm)"""
def codegen(self, wrapper):
# Parser the inputs and constant
args = [x.codegen_reference() for x in self.inputs]
const_args = []
const_args.extend(self.codegen_const_args())
x = args[0]
packed_weight = args[1]
bias = args[2] if self.has_bias else const_args[0]
w_scale, w_zp = args[-2], args[-1]
(
stride,
padding,
dilation,
groups,
x_scale,
x_zp,
o_inv_scale,
o_zp,
output_dtype,
unary_attr,
unary_scalars,
unary_algorithm,
) = const_args[-12:]
codegen_args = (
x,
x_scale,
x_zp,
packed_weight,
w_scale,
w_zp,
bias,
stride,
padding,
dilation,
groups,
o_inv_scale,
o_zp,
output_dtype,
unary_attr,
unary_scalars,
unary_algorithm,
)
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.codegen_kernel_name(),
codegen_args,
self.cpp_op_schema,
self.cpp_kernel_key,
)
if isinstance(self.layout, Layout):
self.codegen_size_asserts(wrapper)
@classmethod
def create(
cls,
x: "TensorBox",
x_scale: float,
x_zp: int,
weight: "TensorBox", # packed_weight
w_scale: "TensorBox",
w_zp: "TensorBox",
bias: "TensorBox",
stride_: List[int],
padding_: List[int],
dilation_: List[int],
groups: int,
o_inv_scale: float,
output_zero_point: int,
output_dtype,
unary_attr,
unary_scalars,
unary_algorithm,
):
transposed = False
output_padding = None
(inputs, constant_args, kernel_layout, _) = _prepare_convolution_fusion_create(
cls,
x,
weight,
bias,
padding_,
stride_,
dilation_,
groups,
transposed,
output_padding,
)
# swap padding and stride to align with functional conv arg order
if bias is None:
constant_args[1], constant_args[2] = constant_args[2], constant_args[1]
else:
constant_args[0], constant_args[1] = constant_args[1], constant_args[0]
w_scale.realize()
w_zp.realize()
inputs = inputs + [w_scale, w_zp]
constant_args = constant_args + [
x_scale,
x_zp,
o_inv_scale,
output_zero_point,
output_dtype,
unary_attr,
may_convert_to_optional(unary_scalars),
unary_algorithm,
]
if output_dtype is not None:
assert output_dtype in [torch.float32, torch.bfloat16]
# in _prepare_convolution_fusion_create, we use x.dtype (uint8) to create kernel_layout
# if we set output_dtype is not None, the output buf should be output_dtype instead of uint8.
kernel_layout.dtype = output_dtype
return QConvPointWisePT2E(
layout=kernel_layout,
inputs=inputs,
constant_args=constant_args,
)
class QConvPointWiseBinaryPT2E(ExternKernelAlloc):
def __init__(
self,
layout,
inputs,
constant_args=(),
):
"""
Needs input/weight/output qparams
if bias is not None
- inputs = [x, w, b, accum, w_scale, w_zp]
- const_args = [stride, padding, dilation, groups, x_scale, x_zp, accum_scale, accum_zp, o_inv_scale, o_zp,
fp32_output, binary_attr, aplha, unary_attr, unary_scalars, unary_algorithm]
else
- inputs = [x, w, accum, w_scale, w_zp]
- const_args = const_args is: [bias, stride, padding, dilation, groups, x_scale, x_zp, accum_scale,
accum_zp, o_inv_scale, o_zp, fp32_output, binary_attr, aplha, unary_attr, unary_scalars, unary_algorithm]
"""
self.has_bias = len(inputs) == 6
super().__init__(
layout,
inputs,
constant_args,
None,
kernel="torch.ops.onednn.qconv2d_pointwise.binary",
cpp_kernel="onednn::qconv2d_pointwise",
)
self.cpp_kernel_overload_name = "binary"
self.cpp_kernel_key = "qconv2d_pointwise_binary"
self.cpp_op_schema = """
at::Tensor(
at::Tensor act,
double act_scale,
int64_t act_zero_point,
at::Tensor accum,
double accum_scale,
int64_t accum_zero_point,
at::Tensor weight,
at::Tensor weight_scales,
at::Tensor weight_zero_points,
c10::optional<at::Tensor> bias,
torch::List<int64_t> stride,
torch::List<int64_t> padding,
torch::List<int64_t> dilation,
int64_t groups,
double inv_output_scale,
int64_t output_zero_point,
c10::optional<c10::ScalarType> output_dtype,
c10::string_view binary_attr,
c10::optional<at::Scalar> alpha,
c10::optional<c10::string_view> attr,
torch::List<c10::optional<at::Scalar>> scalars,
c10::optional<c10::string_view> algorithm)"""
def codegen(self, wrapper):
# Parser the inputs and constant
args = [x.codegen_reference() for x in self.inputs]
const_args = []
const_args.extend(self.codegen_const_args())
x = args[0]
packed_weight = args[1]
bias = args[2] if self.has_bias else const_args[0]
accum, w_scale, w_zp = args[-3], args[-2], args[-1]
(
stride,
padding,
dilation,
groups,
x_scale,
x_zp,
accum_scale,
accum_zp,
o_inv_scale,
o_zp,
output_dtype,
binary_attr,
alpha,
unary_attr,
unary_scalars,
unary_algorithm,
) = const_args[-16:]
conv_args = (
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_zp,
output_dtype,
binary_attr,
alpha,
unary_attr,
unary_scalars,
unary_algorithm,
)
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.codegen_kernel_name(),
conv_args,
self.cpp_op_schema,
self.cpp_kernel_key,
self.cpp_kernel_overload_name,
)
if isinstance(self.layout, Layout):
self.codegen_size_asserts(wrapper)
@classmethod
def create(
cls,
x: "TensorBox",
x_scale,
x_zp,
accum: "TensorBox",
accum_scale,
accum_zp,
weight: "TensorBox", # packed_weight
w_scale,
w_zp,
bias: "TensorBox",
stride_: List[int],
padding_: List[int],
dilation_: List[int],
groups: int,
o_inv_scale: "TensorBox",
output_zero_point: "TensorBox",
output_dtype,
binary_attr,
alpha,
unary_attr,
unary_scalars,
unary_algorithm,
):
transposed = False
output_padding = None
(
inputs,
constant_args,
kernel_layout,
req_stride_order,
) = _prepare_convolution_fusion_create(
cls,
x,
weight,
bias,
padding_,
stride_,
dilation_,
groups,
transposed,
output_padding,
)
accum = cls.require_stride_order(accum, req_stride_order)
inputs.append(accum)
# swap padding and stride to align with functional conv arg order
if bias is None:
constant_args[1], constant_args[2] = constant_args[2], constant_args[1]
else:
constant_args[0], constant_args[1] = constant_args[1], constant_args[0]
w_scale.realize()
w_zp.realize()
inputs = inputs + [w_scale, w_zp]
constant_args = constant_args + [
x_scale,
x_zp,
accum_scale,
accum_zp,
o_inv_scale,
output_zero_point,
output_dtype,
binary_attr,
alpha,
unary_attr,
may_convert_to_optional(unary_scalars),
unary_algorithm,
]
if output_dtype is not None:
# in _prepare_convolution_fusion_create, we use x.dtype (uint8) to create kernel_layout
# if output_dtype is not None, the output buf should be dtype output_dtype instead of uint8.
kernel_layout.dtype = output_dtype
return QConvPointWiseBinaryPT2E(
layout=kernel_layout,
inputs=inputs,
constant_args=constant_args,
)
class QLinearPointwisePT2E(ExternKernelAlloc):
def __init__(
self,
layout,
inputs,
constant_args=(),
):
"""
if bias is not None
- inputs = [x, w, b, weight_scale, weight_zp]
- const_args is: [x_scale, x_zp, o_inv_scale, o_zp,
fp32_output, unary_attr, unary_scalars, unary_algorithm]
else
- inputs = [x, w, weight_scale, weight_zp]
- const_args is: [bias, x_scale, x_zp, o_inv_scale, o_zp,
fp32_output, unary_attr, unary_scalars, unary_algorithm]
"""
self.has_bias = len(inputs) == 5
super().__init__(
layout,
inputs,
constant_args,
None,
kernel="torch.ops.onednn.qlinear_pointwise",
cpp_kernel="onednn::qlinear_pointwise",
)
self.cpp_kernel_key = "qlinear_pointwise"
self.cpp_op_schema = """
at::Tensor(
at::Tensor act,
double act_scale,
int64_t act_zero_point,
at::Tensor weight,
at::Tensor weight_scales,
at::Tensor weight_zero_points,
c10::optional<at::Tensor> bias,
double inv_output_scale,
int64_t output_zero_point,
c10::optional<c10::ScalarType> output_dtype,
std::string post_op_name,
torch::List<c10::optional<at::Scalar>> post_op_args,
std::string post_op_algorithm)"""
def codegen(self, wrapper):
# Parser the inputs and constant
args = [x.codegen_reference() for x in self.inputs]
const_args = []
const_args.extend(self.codegen_const_args())
x = args[0]
packed_weight = args[1]
bias = args[2] if self.has_bias else const_args[0]
w_scale, w_zp = args[-2], args[-1]
(
x_scale,
x_zp,
o_inv_scale,
o_zp,
output_dtype,
unary_attr,
unary_scalars,
unary_algorithm,
) = const_args[-8:]
codegen_args = (
x,
x_scale,
x_zp,
packed_weight,
w_scale,
w_zp,
bias,
o_inv_scale,
o_zp,
output_dtype,
unary_attr,
unary_scalars,
unary_algorithm,
)
wrapper.generate_extern_kernel_alloc_and_find_schema_if_needed(
self.get_name(),
self.codegen_kernel_name(),
codegen_args,
self.cpp_op_schema,
self.cpp_kernel_key,
)
if isinstance(self.layout, Layout):
self.codegen_size_asserts(wrapper)
@classmethod
def create(
cls,
x: "TensorBox",
x_scale: float,
x_zp: int,
weight: "TensorBox", # packed_weight
w_scale: "TensorBox",
w_zp: "TensorBox",
bias: "TensorBox",
o_inv_scale: float,
output_zero_point: int,
output_dtype,
unary_attr,
unary_scalars,
unary_algorithm,
):
(inputs, constant_args, kernel_layout, _) = _prepare_linear_fusion_create(
cls,
x,
weight,
bias,
)
w_scale.realize()
w_zp.realize()
inputs = inputs + [w_scale, w_zp]
constant_args = constant_args + [
x_scale,
x_zp,
o_inv_scale,
output_zero_point,
output_dtype,
unary_attr,
may_convert_to_optional(unary_scalars),
unary_algorithm,
]
if output_dtype is not None:
assert output_dtype in [torch.float32, torch.bfloat16]
# in _prepare_linear_fusion_create, we use x.dtype (uint8) to create kernel_layout
# if we set fp32_output, the output buf should be dtype float32 instead of uint8.
kernel_layout.dtype = output_dtype
return QLinearPointwisePT2E(
layout=kernel_layout,
inputs=inputs,
constant_args=constant_args,
)
@dataclasses.dataclass
class MutableBox(IRNode):
"""
TensorBox / StorageBox allow in-place mutation of Tensors
"""
data: IRNode
def __getattr__(self, name):
fn = getattr(self.data, name)
if callable(fn):
return fn
raise AttributeError(f"{type(self.data).__name__}.{name} not callable")
def realize(self):
return self.data.realize()
def codegen_reference(self, writer=None):
return self.data.codegen_reference(writer)
@property
def layout(self):
return self.data.layout # type: ignore[attr-defined]
def get_layout(self):
return self.layout
def get_size(self):
return self.data.get_size()
def __str__(self):
if isinstance(self.data, MutableBox):
line0 = f"{type(self).__name__}({type(self.data).__name__}("
endl = "))"
inner = self.data.data
else:
line0 = f"{type(self).__name__}("
inner = self.data
endl = ")"
lines = [
line0,
indent(str(inner)),
endl,
]
return "\n".join(lines)
__repr__ = __str__
class TensorBox(MutableBox):
@staticmethod
def create(data):
return TensorBox(StorageBox(data))
class StorageBox(MutableBox):
def is_input_buffer(self):
if isinstance(self.data, (InputBuffer, ReinterpretView)):
return self.data.get_name() in V.graph.graph_inputs
return False
def realize(self):
if isinstance(
self.data,
(
ComputedBuffer,
InputsKernel,
InputBuffer,
ReinterpretView,
TemplateBuffer,
),
):
return self.data.get_name()
assert isinstance(self.data, (Pointwise, Reduction, Scan)), type(self.data)
origin_node = self.data.get_origin_node()
traceback = self.data.get_traceback()
self.data = ComputedBuffer(
name=None,
layout=FlexibleLayout(
device=self.data.get_device(),
dtype=self.data.get_dtype(),
size=self.data.get_size(),
),
data=self.data,
)
self.data.name = V.graph.register_buffer(self.data)
self.data.origins = self.origins
self.data.origin_node = origin_node
self.data.traceback = traceback
return self.data.name
def realize_hint(self):
"""
Called on buffers we expect to be forced to realize later.
"""
if (
isinstance(self.data, (Pointwise, Reduction))
and self.num_reads() > 1
and self.is_pointwise_non_scalar_tensor_num_reads_larger_than_one()
):
self.realize()
def has_exceeded_max_reads(self):
return isinstance(self.data, Pointwise) and (
self.num_reads() > config.realize_acc_reads_threshold
or self.inner_fn_str_len() > config.realize_bytes_threshold
)
def mark_reuse(self, users):
"""
A heuristic to decide if we should realize a tensor
that is used multiple times.
"""
def should_realize_on_cpu(loops: Union[Pointwise, Reduction]):
"""
The heuristic for realizing reused result of heavy ops on cpu
"""
heavy_ops = ["exp"] # a list of heavy ops
fn_str = loops.inner_fn_str()
return any((op + "(") in fn_str for op in heavy_ops)
if (
users > 1
and isinstance(self.data, (Pointwise, Reduction))
and (
self.num_reads() > config.realize_reads_threshold
or len(self.inner_fn_str()) > config.realize_bytes_threshold
or (is_cpu(self.data) and should_realize_on_cpu(self.data))
)
):
self.realize()
@cache_on_self
def num_reads(self):
data = self.data
if isinstance(data, (InputsKernel, InputBuffer, ReinterpretView)):
return 1
if isinstance(data, ComputedBuffer):
read_writes = data.get_read_writes()
else:
assert isinstance(data, (Pointwise, Reduction)), type(data)
read_writes = ComputedBuffer(
name=None,
layout=FlexibleLayout(
device=data.get_device(),
dtype=data.get_dtype(),
size=data.get_size(),
),
data=data,
).get_read_writes()
return len(read_writes.reads)
@cache_on_self
def is_pointwise_non_scalar_tensor_num_reads_larger_than_one(self):
# Skip the check for non Pointwise instances
return (
(sum(read.index != 0 for read in self.data.get_reads()) > 1)
if isinstance(self.data, Pointwise)
and all(
not isinstance(read, dependencies.StarDep)
for read in self.data.get_reads()
)
else True
)
class InterpreterShim(torch.fx.Interpreter):
@staticmethod
@functools.lru_cache(None)
def _dummy_gm():
return torch.fx.symbolic_trace(identity)
def __init__(self, graph, submodules):
# call super() with a placeholder to avoid constructing a
# GraphModule which is very expensive (it does codegen).
super().__init__(self._dummy_gm(), garbage_collect_values=False)
self.module = self
self.graph = graph
self.submodules = submodules
self.extra_traceback = False
self.fetch_attr = submodules.__getitem__
self.current_node = None
def run_node(self, n: torch.fx.Node) -> Any:
self.current_node = n
return super().run_node(n)
def run(self, *args, **kwargs):
with V.set_interpreter_handler(self):
return super().run(*args, **kwargs)
class LoopBody:
"""
Captures the body of a Loops subclass into an FX graph. Persists any
indexing simplifications and makes it easier to analyze loop bodies.
"""
def __init__(self, fn, args, var_ranges):
super().__init__()
self.var_ranges = var_ranges
self.indexing_exprs = {}
self.indexing_exprs_name = {}
self.reads = []
self.writes = []
self.reads_name2expr = {}
self.writes_name2expr = {}
self.other = []
self.submodules = {"get_index": self.get_index}
self.subblocks = {}
self.indirect_vars = []
self.root_block = LoopBodyBlock(self, fn, args)
self.indexing = None
@cache_on_self
def get_nodes(self):
all_graphs = itertools.chain(
(self.root_block.graph,),
(block.graph for block in self.subblocks.values()),
)
return [node for graph in all_graphs for node in graph.nodes]
@cache_on_self
def bounds(self):
# Doing a local import to avoid dumping all the code here
from .bounds import BoundVars
return BoundVars(self)
def debug_str(self):
lines = [f"var_ranges = {dict(self.var_ranges)}"]
lines.extend([f"{name} = {val}" for name, val in self.indexing_exprs.items()])
lines.extend(
[
block.debug_str(name)
for name, block in itertools.chain(
[("body", self.root_block)], self.subblocks.items()
)
]
)
return "\n".join(lines)
def add_index_expr(self, expr: sympy.Expr, category, buf_name):
getattr(self, category).append(expr)
if buf_name is not None:
getattr(self, f"{category}_name2expr")[buf_name] = expr
if expr not in self.indexing_exprs_name:
name = f"index{len(self.indexing_exprs)}"
self.indexing_exprs_name[expr] = name
self.indexing_exprs[name] = expr
return self.indexing_exprs_name[expr]
def add_submodule(self, block, prefix):
"""Not actually for nn.Modules, but subblocks in generated code are mapped to FX call_module opcodes"""
if prefix[-1].isnumeric() and prefix not in self.submodules:
name = prefix
else:
name = f"{prefix}{len(self.submodules)}"
self.submodules[name] = block
return name
def add_indirect(self, size):
name = f"indirect{len(self.indirect_vars)}"
var = sympy_symbol(name)
self.indirect_vars.append(var)
return var
def replace_indirect(self, old, new):
"""Swap in a variable used in indirect indexing"""
if str(old) == str(new):
return
assert self.indexing is not None
self.indexing = {k: sympy_subs(v, {old: new}) for k, v in self.indexing.items()}
def get_index(self, name):
assert self.indexing is not None
return self.indexing[name]
def __call__(self, *indices):
index = list(itertools.chain(*indices))
assert len(index) == len(self.var_ranges), (index, self.var_ranges)
assert all(v not in self.var_ranges for v in index)
replacements = dict(zip(self.var_ranges.keys(), index))
self.indexing = {
name: sympy_subs(expr, replacements)
for name, expr in self.indexing_exprs.items()
}
result = self.root_block()
self.indexing = None
return result
class LoopBodyBlock:
"""
Captures the body of a Loops subclass into an FX graph.
In normal cases there will be a 1:1 mapping between LoopBody and
LoopBodyBlock, hower in the case of ops.masked() the masked out
operations will manifest as an extra LoopBodyBlock.
"""
def __init__(self, body: LoopBody, fn: Callable[..., Any], args: List[Any]):
self.body = body
def add_index(expr, category, buf_name=None):
return tracer.create_proxy(
"call_module",
"get_index",
(self.body.add_index_expr(expr, category, buf_name),),
{},
)
class CaptureIndexing(V.WrapperHandler): # type: ignore[name-defined]
self.name = "CaptureIndexing"
def load(self, name: str, index: sympy.Expr):
index = add_index(index, "reads", name)
return self._inner.load(name, index)
def store(self, name, index, value, mode=None):
index = add_index(index, "writes", name)
return self._inner.store(name, index, value, mode)
def store_reduction(self, name, index, value):
index = add_index(index, "writes", name)
return self._inner.store_reduction(name, index, value)
def reduction(self, dtype, src_dtype, reduction_type, value):
result = self._inner.reduction(dtype, src_dtype, reduction_type, value)
if "welford" in reduction_type:
return tuple(result[i] for i in range(3))
return result
def index_expr(self, index, dtype):
if isinstance(index, (int, sympy.Integer)):
return self._inner.constant(int(index), dtype)
index = add_index(index, "other")
return self._inner.index_expr(index, dtype)
def bucketize(
self,
values,
offsets_name: str,
offsets_size: sympy.Expr,
indexing_dtype: torch.dtype,
right: bool,
):
offsets_size = add_index(offsets_size, "other")
return self._inner.bucketize(
values, offsets_name, offsets_size, indexing_dtype, right
)
@staticmethod
def masked(mask_proxy, masked_body: Callable[..., Any], other_proxy):
"""
Recursively capture the masked out body in another LoopBodyBlock
"""
subblock: LoopBodyBlock
def shim(mask, other):
return V.ops.masked(mask, subblock, other)
name = self.body.add_submodule(shim, "masked_subblock")
subblock = LoopBodyBlock(self.body, masked_body, [])
self.body.subblocks[name] = subblock
return tracer.create_proxy(
"call_module", name, (mask_proxy, other_proxy), {}
)
@staticmethod
def scan(
dtype_proxy, combine_fn: Callable[..., Any], value_proxy, init_proxy
):
def shim(dtype, value, init):
return V.ops.scan(dtype, combine_fn, value, init)
name = self.body.add_submodule(shim, "scan")
return tracer.create_proxy(
"call_module", name, (dtype_proxy, value_proxy, init_proxy), {}
)
@staticmethod
def indirect_indexing(index_proxy, size, check=True):
"""
Flow data from tensors into indexing formulas.
Introduce a call_module to update the indexing.
"""
var = self.body.add_indirect(size)
def set_indirect(new_var):
self.body.replace_indirect(
var, V.ops.indirect_indexing(new_var, size, check)
)
tracer.create_proxy(
"call_module",
self.body.add_submodule(set_indirect, f"set_{var}"),
(index_proxy,),
{},
)
return var
@staticmethod
def output(result):
tracer.create_proxy("output", "output", (result,), {})
tracer = torch.fx.Tracer()
tracer.graph = torch.fx.Graph(tracer_cls=tracer.__class__)
proxy_ops = tracer.create_proxy("placeholder", "ops", (), {})
from .index_propagation import IndexPropagation
from .sizevars import SimplifyIndexing
handler: Any = SimplifyIndexing(
CaptureIndexing(proxy_ops), self.body.var_ranges
)
if config.constant_and_index_propagation:
handler = IndexPropagation(handler)
with V.set_ops_handler(handler):
# This indirection is just a cute way to get IndexPropagation to
# unwrap the return value.
ops.output(fn(*args))
self.graph = tracer.graph
def __call__(self):
graph = self.graph
submodules = self.body.submodules
return InterpreterShim(graph, submodules).run(V.get_ops_handler())
def debug_str(self, name="block"):
code = torch.fx.GraphModule(self.body.submodules, self.graph).code
return re.sub(
# strip `; del var0` suffixes to make output prettier
r";[^\n]*",
"",
code.strip().replace("def forward(", f"def {name}("),
)
class Wait(ExternKernelAlloc):
"""
Wait should not be used by itself. It should always be constructed in tandem
with a collective op that produces a work to wait on.
"""
def __init__(
self,
layout,
inputs,
constant_args=(),
):
super().__init__(layout, inputs, constant_args)
def should_allocate(self):
return False
def codegen(self, wrapper):
from .codegen.wrapper import ReuseLine
wrapper.add_import_once(
"from torch.distributed._functional_collectives_impl import _wait_tensor"
)
(input_collective,) = (t.codegen_reference() for t in self.inputs)
wrapper.writeline(f"{input_collective} = _wait_tensor({input_collective})")
# wait op still needs to produce a 'buffer' that represents the tensor output.
# this is a symbolic gesture, and it gets handled by WrapperCodegen.
# codegen outputs a '# reuse' line that assigns the input buffer here ('input_collective')
# to a new name (`self.get_name()`) and `del`s the old name.
wrapper.writeline(ReuseLine(wrapper, self.inputs[0], self, delete_old=False))
@classmethod
def create(cls, collective_op: "TensorBox"):
# TODO(whc) i'm not sure what's going on here, this probably means I missed something upstream
collective_op.decide_layout()
return Wait(
layout=AliasedLayout(collective_op),
inputs=[collective_op],
)
def get_alias_names(self):
# Signal to codegen that our output buffer isn't safe to reuse
return [self.inputs[0].codegen_reference()]
def get_mutation_names(self):
# The generated `_wait_tensor` op mutates the input tensor
return [self.inputs[0].codegen_reference()]
class CollectiveKernel(ExternKernel):
"""
Each collective should follow the pattern:
- extend InPlaceCollectiveKernel or OutOfPlaceCollectiveKernel.
- the kernel delegates into c10d processgroup, which returns a 'work' obj
- the work obj is registered via _register_tensor_work so it can be waited on later
"""
def __init__(self, layout, inputs, constant_args):
super().__init__(None, layout, inputs, constant_args)
self.name = V.graph.register_buffer(self)
def should_emit_register_tensor_work(self):
return True
def should_emit_find_or_create_pg(self):
return True
def codegen_collective(self, wrapper, output_name, input_names):
# factor so the boilerplate can be handled in CollectiveKernel.codegen
raise NotImplementedError("Must implement")
def codegen_output(self, wrapper, output_name, input_names):
# factor so the boilerplate can be handled in CollectiveKernel.codegen
raise NotImplementedError("Must implement")
@classmethod
def wrap_inputs_as_inplace(cls, inputs):
def wrap_input(var):
op = InPlaceHint(
FlexibleLayout(var.get_device(), var.get_dtype(), var.get_size()), var
)
return TensorBox.create(op)
return list(map(wrap_input, inputs))
def codegen(self, wrapper):
wrapper.add_import_once("import torch.distributed as dist")
wrapper.add_import_once("import torch.distributed.distributed_c10d as c10d")
wrapper.add_import_once(
"import torch.distributed._functional_collectives_impl as fun_col_impl"
)
# extract references to our args in string form for codegen output
input_names = [t.codegen_reference() for t in self.inputs]
output_name = self.get_name()
tag, ranks, group_size = self.constant_args
if self.should_emit_find_or_create_pg():
# TODO: avoid more than one ref of the same pg (even though they are cached inside the api)
wrapper.writeline(
f"{output_name}_pg = c10d._find_or_create_pg_by_ranks_and_tag('{tag}', {ranks}, {group_size})"
)
self.codegen_output(wrapper, output_name, input_names)
self.codegen_collective(wrapper, output_name, input_names)
if self.should_emit_register_tensor_work():
wrapper.writeline(
f"fun_col_impl._register_tensor_work({output_name}, {output_name}_work)"
)
class InPlaceCollectiveKernel(CollectiveKernel):
"""
InPlaceCollectiveKernel are those with in-out arguments such as all_reduce.
Extend this kernel if your collective needs to modify its inputs in-place.
"""
def __init__(self, layout, inputs, constant_args):
super().__init__(layout, inputs, constant_args)
def should_allocate(self):
return False
def has_side_effects(self):
return True
def codegen_output(self, wrapper, output_name, input_names):
if len(input_names) > 1:
wrapper.writeline(f"{output_name} = [{','.join(input_names)}] ")
else:
wrapper.writeline(f"{output_name} = {input_names[0]}")
class OutOfPlaceCollectiveKernel(CollectiveKernel):
"""
OutOfPlaceCollectiveKernel are those that allocate their
outputs and leave their inputs inplace, such as all_gather.
"""
def __init__(self, layout, inputs, outputs, constant_args):
super().__init__(layout, inputs + outputs, constant_args)
self.outputs = outputs
self.original_inputs = inputs
# NOTE: As seen in issue #108780, output buffers of out-of-place collectives
# could be incorrectly reused. As a safety measure, here we just ban the reuse of them.
# TODO: A better fix is to figure out how to propagate the aliases properly,
# so that the buffer is only reused after all its users have consumed it.
for x in self.outputs:
V.graph.never_reuse_buffers.add(x.name)
def should_allocate(self):
return False
def has_side_effects(self):
return True
def codegen_output(self, wrapper, output_name, input_names):
input_names = [t.codegen_reference() for t in self.original_inputs]
wrapper.writeline(f"{output_name}_inputs = [{','.join(input_names)}]")
wrapper.writeline(f"{output_name} = [{','.join(x.name for x in self.outputs)}]")
@classmethod
def create_output_buffers(cls, inputs, size_cb=None):
outputs = []
for input in inputs:
new_size = input.get_size()
if size_cb is not None:
size_cb(new_size)
# new_size[0] *= group_size
buff = OutputBuffer(
layout=FlexibleLayout(
device=input.get_device(),
dtype=input.get_dtype(),
size=new_size,
),
)
outputs.append(buff)
return outputs
@classmethod
def create_output_nodes(cls, coll, output_buffers):
return [
MultiOutputNoSizeAssert(
out_t.layout,
coll,
f"[{i}]",
)
for i, out_t in enumerate(output_buffers)
]
class InPlaceHint(ExternKernel):
"""
Helper OP to encode an in/out argument that tries to make it inplace whenever possible.
Wrap the input of your inplace op to enable this behavior.
The design is based on two key decisions:
- this node is responsible for allocating the in/out buffer used by the collective.
This is controlled by the ``should_allocate`` method that returns True here and
False for the collective node
- The scheduler special-case this node and enable it to reuse its input.
"""
def codegen(self, wrapper):
input_name = self.inputs[0].codegen_reference()
output_name = self.get_name()
if not wrapper.did_reuse(self, self.inputs[0]):
wrapper.writeline(f"{output_name}.copy_({input_name}) #no reuse")
def __init__(self, layout, input):
input = self.realize_input(input)
super().__init__(None, layout, self.unwrap_storage([input]), ())
self.name = V.graph.register_buffer(self)
def should_allocate(self):
return True
class OutputBuffer(ExternKernel):
"""
Represent the output buffer used by ops that require multiple of them
"""
def __init__(self, layout):
super().__init__(name=None, layout=layout, inputs=[])
self.name = V.graph.register_buffer(self)
def should_allocate(self):
return True
def codegen(self, wrapper):
wrapper.writeline(f"# collective out buffer {self.name}")
class MultiOutputNoSizeAssert(MultiOutput):
"""
Extract partial output from a multi-output OP.
Works like MultiOutput but doesn't assert size. This must be a property guaranteed by the op emitting this.
"""
def __init__(self, layout, input, index):
super().__init__(layout, input, [])
self.index = index
def codegen(self, wrapper):
wrapper.writeline(
f"{self.get_name()} = {self.inputs[0].get_name()}{self.index}"
)
class Broadcast(InPlaceCollectiveKernel):
def __init__(self, layout, inputs, constant_args, src):
super().__init__(layout, inputs, constant_args)
self.src = src
def get_mutation_names(self):
return [self.inputs[0].get_name()]
def get_unbacked_symbol_defs(self):
return {}
@classmethod
def create(
cls, x: "TensorBox", src: int, tag: str, ranks: List[int], group_size: int
):
inplace_inputs = cls.wrap_inputs_as_inplace([x])
packed = Broadcast(
layout=NoneLayout(inplace_inputs[0].get_device()), # type: ignore[arg-type]
inputs=inplace_inputs,
constant_args=[tag, ranks, group_size],
src=src,
)
mark_node_as_mutating(packed, inplace_inputs[0])
return inplace_inputs[0]
def codegen_collective(self, wrapper, output_name, input_names):
wrapper.writeline(
f"{output_name}_work = dist.broadcast("
f"{output_name}, async_op=True, group={output_name}_pg, src={self.src})"
)
class AllReduceCoalesced(InPlaceCollectiveKernel):
def __init__(self, layout, inputs, constant_args, reduce_op):
super().__init__(layout, inputs, constant_args)
self.reduce_op = reduce_op
def should_allocate(self):
return False
def get_mutation_names(self):
return [self.inputs[0].get_name()]
def get_unbacked_symbol_defs(self):
return {}
@classmethod
def create(
cls,
inputs: List["TensorBox"],
reduce_op: str,
tag: str,
ranks: List[int],
group_size: int,
):
inplace_inputs = cls.wrap_inputs_as_inplace(inputs)
packed = AllReduceCoalesced(
layout=NoneLayout(inplace_inputs[0].get_device()), # type: ignore[arg-type]
inputs=inplace_inputs,
constant_args=[tag, ranks, group_size],
reduce_op=reduce_op,
)
mark_node_as_mutating(packed, inplace_inputs[0])
return inplace_inputs
def codegen_collective(self, wrapper, output_name, input_names):
wrapper.writeline(
f"{output_name}_work = dist.all_reduce_coalesced("
f"{output_name}, "
f"op=fun_col_impl._str_to_reduce_op('{str(self.reduce_op)}'), "
f"group={output_name}_pg, "
"async_op=True)"
)
class AllReduce(InPlaceCollectiveKernel):
def __init__(self, layout, inputs, constant_args, reduce_op):
super().__init__(layout, inputs, constant_args)
self.reduce_op = reduce_op
def get_mutation_names(self):
return [self.inputs[0].get_name()]
def get_unbacked_symbol_defs(self):
return {}
@classmethod
def create(
cls, x: "TensorBox", reduce_op: str, tag: str, ranks: List[int], group_size: int
):
inplace_inputs = cls.wrap_inputs_as_inplace([x])
packed = AllReduce(
layout=NoneLayout(inplace_inputs[0].get_device()), # type: ignore[arg-type]
inputs=inplace_inputs,
constant_args=[tag, ranks, group_size],
reduce_op=reduce_op,
)
mark_node_as_mutating(packed, inplace_inputs[0])
return inplace_inputs[0]
def codegen_collective(self, wrapper, output_name, input_names):
wrapper.writeline(
f"{output_name}_work = dist.all_reduce("
f"{output_name}, async_op=True, group={output_name}_pg, op=fun_col_impl._str_to_reduce_op('{str(self.reduce_op)}'))"
)
class AllGatherIntoTensor(OutOfPlaceCollectiveKernel):
def __init__(self, layout, inputs, outputs, constant_args):
super().__init__(layout, inputs, outputs, constant_args)
@classmethod
def create(cls, x: "TensorBox", tag: str, ranks: List[int], group_size: int):
inputs = [cls.realize_input(x)]
def compute_size(new_size):
new_size[0] *= group_size
outputs = cls.create_output_buffers(inputs, compute_size)
layout = MultiOutputLayout(inputs[0].get_device())
packed = AllGatherIntoTensor(
layout=layout,
inputs=inputs,
outputs=outputs,
constant_args=[tag, ranks, group_size],
)
return cls.create_output_nodes(packed, outputs)[0]
def codegen_collective(self, wrapper, output_name, input_names):
wrapper.writeline(
f"{output_name}_work = dist.all_gather_into_tensor("
f"{output_name}[0], {output_name}_inputs[0], async_op=True, group={output_name}_pg)"
)
class ReduceScatterTensor(OutOfPlaceCollectiveKernel):
def __init__(self, layout, inputs, outputs, constant_args, reduce_op):
super().__init__(layout, inputs, outputs, constant_args)
self.reduce_op = reduce_op
@classmethod
def create(
cls,
x: "TensorBox",
reduce_op: str,
tag: str,
ranks: List[int],
group_size: int,
):
inputs = [cls.realize_input(x)]
def compute_size(new_size):
new_size[0] //= group_size
outputs = cls.create_output_buffers(inputs, compute_size)
layout = MultiOutputLayout(inputs[0].get_device())
packed = ReduceScatterTensor(
layout=layout,
inputs=inputs,
outputs=outputs,
constant_args=[tag, ranks, group_size],
reduce_op=reduce_op,
)
return cls.create_output_nodes(packed, outputs)[0]
def codegen_collective(self, wrapper, output_name, input_names):
wrapper.writeline(
f"{output_name}_work = dist.reduce_scatter_tensor("
f"{output_name}[0], {output_name}_inputs[0], "
f"async_op=True, group={output_name}_pg, op=fun_col_impl._str_to_reduce_op('{str(self.reduce_op)}'))"
)
class AllGatherIntoTensorCoalesced(OutOfPlaceCollectiveKernel):
def __init__(self, layout, inputs, outputs, constant_args):
super().__init__(layout, inputs, outputs, constant_args)
@classmethod
def create(
cls,
inputs: List["TensorBox"],
tag: str,
ranks: List[int],
group_size: int,
):
inputs = [cls.realize_input(x) for x in inputs]
def compute_size(new_size):
new_size[0] *= group_size
outputs = cls.create_output_buffers(inputs, compute_size)
layout = MultiOutputLayout(inputs[0].get_device())
packed = AllGatherIntoTensorCoalesced(
layout=layout,
inputs=inputs,
outputs=outputs,
constant_args=[tag, ranks, group_size],
)
return outputs
# return cls.create_output_nodes(packed, outputs)
def codegen_collective(self, wrapper, output_name, input_names):
wrapper.writeline(
f"{output_name}_work = fun_col_impl._all_gather_into_tensor_coalesced_fallback("
f"output_tensors={output_name}, "
f"input_tensors={output_name}_inputs, "
f"group={output_name}_pg, "
"async_op=True)"
)
class ReduceScatterTensorCoalesced(OutOfPlaceCollectiveKernel):
def __init__(self, layout, inputs, outputs, constant_args, reduce_op):
super().__init__(layout, inputs, outputs, constant_args)
self.reduce_op = reduce_op
@classmethod
def create(
cls,
inputs: List["TensorBox"],
reduce_op: str,
tag: str,
ranks: List[int],
group_size: int,
):
inputs = [cls.realize_input(x) for x in inputs]
def compute_size(new_size):
new_size[0] //= group_size
outputs = cls.create_output_buffers(inputs, compute_size)
layout = MultiOutputLayout(inputs[0].get_device())
_ = ReduceScatterTensorCoalesced(
layout=layout,
inputs=inputs,
outputs=outputs,
constant_args=[tag, ranks, group_size],
reduce_op=reduce_op,
)
return outputs
def codegen_collective(self, wrapper, output_name, input_names):
wrapper.writeline(
f"{output_name}_work = fun_col_impl._reduce_scatter_tensor_coalesced_fallback("
f"output_tensors={output_name}, "
f"input_tensors={output_name}_inputs, "
f"op=fun_col_impl._str_to_reduce_op('{str(self.reduce_op)}'), "
f"group={output_name}_pg, "
"async_op=True)"
)
# TODO(yifu): replace the CollectiveKernel IR hierarchy with _CollectiveKernel.
class _CollectiveKernel(FallbackKernel):
def should_allocate(self):
return False
def has_side_effects(self):
return True
# This is identical to FallbackKernel.set_cpp_kernel(), minus the
# part that checks against input aliasing and mutation.
def set_cpp_kernel(self, kernel):
from .codegen.wrapper import get_cpp_op_schema
self.cpp_kernel = kernel._schema.name
self.cpp_kernel_overload_name = kernel._schema.overload_name
self.cpp_kernel_key = (
f"{self.cpp_kernel.replace('::', '_')}_{self.cpp_kernel_overload_name}"
)
self.cpp_op_schema = get_cpp_op_schema(kernel)
self.ordered_kwargs_for_cpp_kernel = [
x.name for x in kernel._schema.arguments if x.kwarg_only
]
# NOTE: [In-Place Collective Safety]
# Between the initiation and completion of an in-place collective, the
# input buffers are subject to both volatile reads and volatile writes.
# They must not be read, written to or reused by another kernel. To ensure
# the constraints, we model collective -> wait_tensor as as two-step
# mutation of the input buffers.
@classmethod
def create_inplace(
cls, kernel, inputs: Union[TensorBox, List[TensorBox]], *args, **kwargs
) -> None:
with V.graph.fake_mode:
(
example_output,
tensor_args,
non_tensor_args,
unflatten_args,
) = cls.process_kernel(kernel, inputs, *args, **kwargs)
for tensor_arg in tensor_args:
tensor_arg.realize()
packed = cls(
NoneLayout(tensor_args[0].get_device()),
kernel,
tensor_args,
non_tensor_args,
unflatten_args,
)
pytree.tree_map(lambda x: MutationOutput(x.layout, x, packed), inputs)
# NOTE: [Out-of-Place Collective Safety]
# Between the initiation and completion of an out-of-place collective:
#
# Input buffers:
# - Are subject to volatile reads
# - Can be read by another kernel
# - Must not be written to or reused by another kernel
#
# Output buffers:
# - Are subject to volatile writes
# - Must not be read, written to or reused by another kernel
#
# To ensure the safety of input buffers without sacrificing read
# availability, we add input buffers as read deps of wait_tensor kernels.
#
# To ensure the safety of output buffers, we model wait_tensor as a
# mutation to the output buffer. Note we also assumes the user program being
# correct and the output buffer is not consumed by kernels other than
# wait_tensor.
#
# TODO(yifu): add a pre-grad pass to validate the correctness of collective
# usage in the user program.
@classmethod
def create_out_of_place(
cls, kernel, inputs: Union[TensorBox, List[TensorBox]], *args, **kwargs
):
with V.graph.fake_mode:
(
example_output,
tensor_args,
non_tensor_args,
unflatten_args,
) = cls.process_kernel(kernel, inputs, *args, **kwargs)
for tensor_arg in tensor_args:
tensor_arg.realize()
if isinstance(example_output, list):
device = cls.find_device(tensor_args, example_output)
packed = cls(
MultiOutputLayout(device),
kernel,
tensor_args,
non_tensor_args,
unflatten_args,
)
packed.outputs = [
MultiOutput(
cls.tensor_to_layout(tensor),
packed,
[(list, i)],
)
for i, tensor in enumerate(example_output)
]
return packed.outputs
else:
packed = cls(
cls.tensor_to_layout(example_output),
kernel,
tensor_args,
non_tensor_args,
unflatten_args,
)
packed.outputs = [packed]
return packed
class _WaitKernel(_CollectiveKernel):
def get_volatile_reads(self):
inp = self.inputs[0]
if isinstance(inp, _CollectiveKernel):
# Out-of-place single-output
return [inp.inputs[0]]
elif isinstance(inp, MultiOutput):
# Out-of-place multi-output
coll = inp.inputs[0]
assert isinstance(coll, _CollectiveKernel)
_, idx = inp.indices[0]
return [coll.inputs[idx]]
else:
# In-place requires no additional deps handling for volatile
# reads since the inputs are mutated.
return []
@classmethod
def create_wait(cls, kernel, inp: TensorBox) -> None:
with V.graph.fake_mode:
(
example_output,
tensor_args,
non_tensor_args,
unflatten_args,
) = cls.process_kernel(kernel, inp)
packed = cls(
NoneLayout(inp.get_device()),
kernel,
tensor_args,
non_tensor_args,
unflatten_args,
)
MutationOutput(inp.layout, inp, packed)
def get_read_writes(self):
read_writes = super().get_read_writes()
# See [Out-of-Place Collective Safety].
volatile_reads = self.get_volatile_reads()
for vr in volatile_reads:
read_writes.reads.add(dependencies.StarDep(vr.get_name()))
return read_writes
# NB: recursive structure here reflects val_to_arg_str, avoid
# calling free_unbacked_symbols on "exotic" types that don't get pexpr
# treatment
def maybe_free_unbacked_symbols(s):
if isinstance(s, (SymTypes, sympy.Expr)):
# This branch should be impossible in return position
return free_unbacked_symbols(s)
elif isinstance(s, (tuple, list)):
r = set()
for t in s:
r |= maybe_free_unbacked_symbols(t)
return r
elif isinstance(s, torch.Tensor):
# This branch is impossible in constant-args position
return free_unbacked_symbols(s)
else:
return set()
class AllToAllSingle(OutOfPlaceCollectiveKernel):
def __init__(
self,
layout,
inputs,
outputs,
constant_args,
output_split_sizes,
input_split_sizes,
):
super().__init__(layout, inputs, outputs, constant_args)
self.output_split_sizes = output_split_sizes
self.input_split_sizes = input_split_sizes
def get_unbacked_symbol_uses(self):
r = set()
if self.output_split_sizes is not None:
r |= free_unbacked_symbols(self.output_split_sizes)
if self.input_split_sizes is not None:
r |= free_unbacked_symbols(self.input_split_sizes)
return r
@classmethod
def create(
cls,
x: "TensorBox",
output_split_sizes: Optional[List[Expr]],
input_split_sizes: Optional[List[Expr]],
tag: str,
ranks: List[int],
group_size: int,
):
inputs = [cls.realize_input(x)]
def compute_size(new_size):
if output_split_sizes is not None:
new_size[0] = sum(output_split_sizes)
outputs = cls.create_output_buffers(inputs, compute_size)
layout = MultiOutputLayout(inputs[0].get_device())
packed = AllToAllSingle(
layout=layout,
inputs=inputs,
outputs=outputs,
constant_args=[tag, ranks, group_size],
output_split_sizes=output_split_sizes,
input_split_sizes=input_split_sizes,
)
return cls.create_output_nodes(packed, outputs)[0]
def codegen_collective(self, wrapper, output_name, input_names):
tag, ranks, group_size = self.constant_args
# TODO: might be necessary to do some pretty printing on
# split sizes
wrapper.writeline(
f"{output_name}_work = dist.all_to_all_single("
f"{output_name}[0], {output_name}_inputs[0], "
f"output_split_sizes={self.output_split_sizes}, "
f"input_split_sizes={self.input_split_sizes}, "
f"group={output_name}_pg, async_op=True)"
)