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import functools
import math
import operator
import torch
from torch.nested._internal.sdpa import jagged_scaled_dot_product_attention
from .nested_tensor import NestedTensor
from typing import * # noqa: F403
import torch.nn.functional as F
from torch.fx.operator_schemas import normalize_function
__all__: List[Any] = []
JAGGED_OPS_TABLE: Dict[Any, Any] = {}
# Simplifying assumption: we assume that the batch dim is always the left-most
# dim, and the ragged dim is always the second dim.
def _outer_to_inner_dim(ndim, dim):
assert dim >= 0 and dim < ndim
return 0 if dim < 2 else dim - 1
def _wrap_jagged_dim(
ndim, dim, op_name, convert_to_inner_dim=True, allow_batch_dim=False
):
from torch._prims_common import canonicalize_dims
wrapped = canonicalize_dims(ndim, dim)
if wrapped == 1:
raise RuntimeError(f"{op_name}(): not supported for NestedTensor on dim=1")
elif wrapped == 0 and not allow_batch_dim:
raise RuntimeError(f"{op_name}(): not supported for NestedTensor on dim=0")
return _outer_to_inner_dim(ndim, wrapped) if convert_to_inner_dim else wrapped
def _wrap_jagged_dims(ndim, dims, op_name):
# ex: (2, 3, 4) -> (1, 2, 3)
# ex: (0, 1, 4) -> (0, 3)
from torch._prims_common import canonicalize_dims
wrapped_dims = [canonicalize_dims(ndim, d) for d in dims]
# This logic needs to be done after we canonicalize dims but before we
# map to inner dims so we can print a nicer error message.
zero_in_dims = 0 in wrapped_dims
one_in_dims = 1 in wrapped_dims
if zero_in_dims ^ one_in_dims:
apply, not_apply = ("batch", "ragged") if zero_in_dims else ("ragged", "batch")
raise RuntimeError(
f"{op_name}(): applying over the {apply} dimension, but not the {not_apply}"
" dimension is not supported for NestedTensor"
)
return (
tuple(_outer_to_inner_dim(ndim, d) for d in dims if d != 0),
zero_in_dims,
)
def check_schema(schema_str: str, func, *args, **kwargs) -> None:
named_arg_types = schema_str.split(", ")
num_optional_args = sum([x.endswith("?") for x in named_arg_types])
min_args = len(named_arg_types) - num_optional_args
# special case: ellipses allows for any number of unchecked args at the end
if named_arg_types[-1] == "...":
named_arg_types = named_arg_types[:-1]
else:
if not (len(args) >= min_args and len(args) <= len(named_arg_types)):
raise ValueError(
f"NestedTensor {func.__name__}({schema_str}): expected at least {min_args} "
f"arguments and at most {len(named_arg_types)} arguments, but got: "
f"{len(args)} arguments"
)
arg_type_check_fns = {
"t": lambda x: isinstance(x, torch.Tensor) and not isinstance(x, NestedTensor),
"jt": lambda x: isinstance(x, NestedTensor)
and x._lengths is None
and x._ragged_idx == 1, # ops with "jt" require contiguous JT only
"jt_all": lambda x: isinstance(
x, NestedTensor
), # ops with "jt_all" can accept all kinds of JT
"any": lambda x: True,
}
for i, named_arg_type in enumerate(named_arg_types):
name, arg_type = named_arg_type.split(": ")
is_optional = arg_type.endswith("?")
normalized_arg_type = arg_type[:-1] if is_optional else arg_type
if normalized_arg_type not in arg_type_check_fns.keys():
raise AssertionError(f"Unknown arg type: {normalized_arg_type}")
if i >= len(args):
if not is_optional:
raise ValueError(
f"NestedTensor {func.__name__}({schema_str}) "
f"missing required argument: {name}"
)
continue
_check_fn = arg_type_check_fns[normalized_arg_type]
def check_fn(x, is_optional=is_optional):
if is_optional:
return x is None or _check_fn(x)
else:
return _check_fn(x)
if not check_fn(args[i]):
type_to_desc = {
"t": "tensor",
"t?": "optional tensor",
"jt": "contiguous jagged layout NestedTensor",
"jt_all": "jagged layout NestedTensor",
"any": "<any type>",
}
raise ValueError(
f"NestedTensor {func.__name__}({schema_str}): expected {name} to be a "
f"{type_to_desc[arg_type]}"
)
def check_ragged_dim_same(
func, a: NestedTensor, a_name: str, b: NestedTensor, b_name: str
) -> None:
# Calling into .shape here
if a._size[a._ragged_idx] != b._size[b._ragged_idx]:
raise RuntimeError(
f"NestedTensor {func.__name__}: expected {a_name} and {b_name} to have the "
"same exact offsets tensor."
)
# returns True if the raggedness-relevant portions of the NT shape
# match those of the specified size
def raggedness_matches(nt, size):
end = nt._ragged_idx + 1
nt_ragged = nt._size[:end]
size_ragged = size[:end]
return len(nt_ragged) == len(size_ragged) and (
all(ns == s or s == -1 for ns, s in zip(nt_ragged, size_ragged))
)
def squeeze_leading_ones(t):
# Note: [ Squeezing leading ones ]
#
# Squeeze leading ones from t.
#
# We want:
# (B, j0, ?, ?) + (1, 1, ?, ?) -> (B, j0, ?, ?)
# (B, j0, ?, ?) + (1, 1, 1, ?, ?) -> (1, B, j0, ?, ?) (not yet supported)
#
# 1) Squeeze extra ones and grab values from NT
# (1, 1, ?, ?) -> (?, ?) and (sum(*), ?, ?) -> (B, j0, ?, ?)
# 2) Do dense broadcasting:
# (sum(*), ?, ?) + (?, ?) -> (sum(*), ?, ?)
# 3) Construct nested tensor
# (sum(*), ?, ?) -> (B, j0, ?, ?)
#
# If unsqueezing on the 0th dim becomes supported, we would unsqueeze
# at step (4) and we would need to update this function to record how
# many ones we unsqueezed.
while t.shape[0] == 1:
t = t.squeeze(0)
return t
def register_func(tables, aten_ops, schema_str):
if not isinstance(aten_ops, list):
aten_ops = [aten_ops]
if not isinstance(tables, list):
tables = [tables]
def wrapper(func):
for aten_op in aten_ops:
def get_inner(aten_op):
def inner(*args, **kwargs):
check_schema(schema_str, func, *args, **kwargs)
return func(aten_op, *args, **kwargs)
return inner
for table in tables:
table[aten_op] = get_inner(aten_op)
return func
return wrapper
register_jagged_func = functools.partial(register_func, JAGGED_OPS_TABLE)
def lookup_jagged(func, *args, **kwargs) -> Optional[Callable]:
dispatch_func = JAGGED_OPS_TABLE.get(func, None)
if dispatch_func is not None:
return dispatch_func
# Handle pointwise fallbacks
if torch.Tag.pointwise in func.tags:
# Assume there aren't additional tensors that aren't the "unary/binary" args
num_tensor_args = sum([isinstance(x, torch.Tensor) for x in args])
if num_tensor_args == 1:
check_schema("self: jt_all, ...", func, *args, **kwargs)
return functools.partial(jagged_unary_pointwise, func)
elif num_tensor_args == 2:
check_schema("lhs: any, rhs: any, ...", func, *args, **kwargs)
return functools.partial(jagged_binary_pointwise, func)
return None
def extract_kwargs(arg):
kwargs = {
"offsets": arg.offsets(),
"_metadata_cache": arg._metadata_cache,
"_ragged_idx": arg._ragged_idx,
}
return kwargs
def jagged_unary_pointwise(func, *args, **kwargs):
return NestedTensor(
func(args[0]._values, *args[1:], **kwargs), **extract_kwargs(args[0])
)
def jagged_binary_pointwise(func, *args, **kwargs):
a, b = args[0], args[1]
assert isinstance(a, NestedTensor) or isinstance(b, NestedTensor)
mismatch_error_msg = (
"cannot call binary pointwise function {} with inputs of shapes {} and {}"
)
# a is NT, b is NT
if isinstance(a, NestedTensor) and isinstance(b, NestedTensor):
# ex: (B, j0, D) + (B, j0, D)
# ex: (B, j0, D) + (B, j0, 1)
if raggedness_matches(a, b._size):
return NestedTensor(
func(a._values, b._values, *args[2:], **kwargs), **extract_kwargs(a)
)
raise RuntimeError(mismatch_error_msg.format(func.__name__, a._size, b._size))
# either a is NT or b is NT at this point
a_is_nt = isinstance(a, NestedTensor)
extracted_kwargs = extract_kwargs(a) if a_is_nt else extract_kwargs(b)
# === Handle broadcasting across the batch / ragged dims ===
# Easy case: take advantage of pre-existing broadcasting logic
# ex: (B, j0, ?, ?) + (?) -> (B, j0, ?, ?)
# ex: (B, j0, ?, ?) + (?, ?) -> (B, j0, ?, ?)
# ex: (B, j0, ?, ?) + (1, 1, ?, ?) -> (B, j0, ?, ?)
nt, t = (a, b) if a_is_nt else (b, a)
# See Note: [ Squeezing leading ones ]
if t.dim() > nt.dim():
raise NotImplementedError("NYI: broadcasting NT with T with larger dim")
t_squeezed = squeeze_leading_ones(t)
if nt.dim() >= t_squeezed.dim() + 2:
lhs, rhs = (nt._values, t_squeezed) if a_is_nt else (t_squeezed, nt._values)
return NestedTensor(func(lhs, rhs, *args[2:], **kwargs), **extracted_kwargs)
# Harder case: do manual broadcasting over unbound components
# when NT dim == non-NT dim
# ex: (B, j0, D_0, D_1) + (B, 1, D_0, D_1) -> (B, j0, D_0, D_1)
if a.dim() == b.dim():
# ex: (B, j0, D_0, D_1) + (1, 1, D_0, D_1) -> should
# be (B, j0, D_0, D_1) but not yet supported
if a.shape[0] != b.shape[0]:
raise RuntimeError(
mismatch_error_msg.format(func.__name__, a.shape, b.shape)
)
# need to use offsets to broadcast across ragged dim properly
# NB: inefficient fallback here; Triton codegen can help this
# TODO: Make this work with autograd
outputs = []
for a_comp, b_comp in zip(a.unbind(), b.unbind()):
outputs.append(func(a_comp, b_comp, *args[2:], **kwargs))
new_values = torch.cat(outputs, dim=0)
return NestedTensor(new_values, **extracted_kwargs)
# ex: (B, j0, D_0, D_1) + (A, B, 1, D_0, D_1) -> error because this breaks the invariant
# that ragged dim is wrt left-most batch dim
raise RuntimeError(mismatch_error_msg.format(func.__name__, a.shape, b.shape))
def jagged_torch_function(func, *args, **kwargs):
# SDPA has special kernels that handle nested tensors.
# Dispatch to the correct implementation here
if func is torch._C._nn.scaled_dot_product_attention:
return jagged_scaled_dot_product_attention(*args, **kwargs)
# Handle flatten() here because it's CompositeImplicit.
if func.__name__ == "flatten":
def _flatten_sig(input, start_dim=0, end_dim=-1):
pass
_, new_kwargs = normalize_function(
_flatten_sig, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
# NB: stay in outer dim space because we're going to redispatch on a NT input
start_dim = _wrap_jagged_dim(
inp.dim(), new_kwargs["start_dim"], "flatten", convert_to_inner_dim=False
)
end_dim = _wrap_jagged_dim(
inp.dim(), new_kwargs["end_dim"], "flatten", convert_to_inner_dim=False
)
if start_dim == end_dim:
return inp
product = functools.reduce(operator.mul, inp.shape[start_dim : end_dim + 1])
new_shape = (*inp.shape[:start_dim], product, *inp.shape[end_dim + 1 :])
return inp.reshape(*new_shape)
raise NotImplementedError(func)
@register_jagged_func(
[
torch.ops.aten.is_non_overlapping_and_dense.default,
torch.ops.aten.sym_size.default,
torch.ops.aten.dim.default,
torch.ops.aten.sym_numel.default,
torch.ops.aten.sym_stride.default,
torch.ops.aten.sym_storage_offset.default,
],
"self: jt_all",
)
def tensor_attr_supported_getter(func, *args, **kwargs):
if func == torch.ops.aten.is_non_overlapping_and_dense.default:
return False
if func == torch.ops.aten.sym_size.default:
return args[0]._size
if func == torch.ops.aten.dim.default:
return len(args[0]._size)
if func == torch.ops.aten.sym_numel.default:
if args[0]._lengths is not None:
return int(sum(args[0]._lengths) * math.prod(args[0]._size[2:]))
return args[0]._values.numel()
if func == torch.ops.aten.sym_stride.default:
return args[0]._strides
if func == torch.ops.aten.sym_storage_offset.default:
return args[0]._values.storage_offset()
@register_jagged_func(torch.ops.prim.layout.default, "self: jt_all")
def prim_layout_default(func, *args, **kwargs):
return torch.jagged
@register_jagged_func(
[torch.ops.aten.size.default],
"self: jt_all",
)
def tensor_attr_unsupported_getter(func, *args, **kwargs):
if func == torch.ops.aten.size.default:
raise RuntimeError(
"NestedTensors does not support directly calling torch.ops.aten.size "
"please use `nested_tensor.size()` instead."
)
@register_jagged_func(torch.ops.aten.is_contiguous.default, "self: jt_all")
def is_contiguous_general(func, *args, **kwargs):
from torch._prims_common import is_contiguous_for_memory_format
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
# If created from narrow() check for lengths
if inp.lengths() is not None:
return False
new_kwargs["memory_format"] = new_kwargs.get(
"memory_format", torch.contiguous_format
)
if new_kwargs["memory_format"] == torch.preserve_format:
return True
return is_contiguous_for_memory_format(inp._values, **new_kwargs)
register_jagged_func(
torch.ops.aten.is_contiguous.memory_format, "self: jt_all, memory_format: any?"
)(is_contiguous_general)
@register_jagged_func(torch.ops.aten.linear.default, "input: jt, weight: t, bias: t?")
def linear_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
@register_jagged_func(
torch.ops.aten.linear_backward.default,
"self: jt, grad_output: jt, weight: t, output_mask: any",
)
def linear_backward_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
grad_output = new_kwargs.pop("grad_output")
weight = new_kwargs.pop("weight")
check_ragged_dim_same(func, inp, "self", grad_output, "grad_output")
ds = NestedTensor(
torch.mm(grad_output._values, weight), **extract_kwargs(grad_output)
)
dw = torch.mm(grad_output._values.T, inp._values)
db = None # NYI: gradient for bias, need to reduce over ragged dim
return (ds, dw, db)
@register_jagged_func(torch.ops.aten._to_copy.default, "self: jt_all")
def to_copy_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
# don't change layout
new_kwargs.pop("layout")
new_values = func(inp._values, **new_kwargs)
# NB: Purposefully keep offsets on the old device.
return NestedTensor(new_values, **extract_kwargs(inp))
register_jagged_func(
[
torch.ops.aten.empty_like.default,
torch.ops.aten.ones_like.default,
torch.ops.aten.zeros_like.default,
torch.ops.aten.randn_like.default,
torch.ops.aten.detach.default,
],
"self: jt_all",
)(jagged_unary_pointwise)
register_jagged_func(
torch.ops.aten._softmax.default, "self: jt, dim: any, half_to_float: any"
)(jagged_unary_pointwise)
@register_jagged_func(
torch.ops.aten.native_dropout.default, "self: jt, float: any, train: any?"
)
def native_dropout_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
out1, out2 = func(inp._values, **new_kwargs)
return (
NestedTensor(out1, **extract_kwargs(inp)),
NestedTensor(out2, **extract_kwargs(inp)),
)
@register_jagged_func(
torch.ops.aten.native_dropout_backward.default,
"grad_output: jt, mask: jt, scale: any",
)
def native_dropout_backward_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
grad_output = new_kwargs.pop("grad_output")
mask = new_kwargs.pop("mask")
return NestedTensor(
func(grad_output._values, mask._values, **new_kwargs),
**extract_kwargs(grad_output),
)
@register_jagged_func(torch.ops.aten.prod.dim_int, "self: jt, dim: any, keepdim: any?")
def prod_dim_int(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
# TODO: Figure out how to handle this better
# keep_dim is required to keep it in jagged format
if not new_kwargs["keepdim"]:
raise RuntimeError("prod(): keepdim=True must be set for NestedTensor")
dim = new_kwargs["dim"]
new_kwargs["dim"] = _wrap_jagged_dim(len(inp._size), dim, "prod")
return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(args[0]))
@register_jagged_func(
torch.ops.aten.split.Tensor, "self: jt, split_size: any, dim: any"
)
def split_tensor(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
new_kwargs["dim"] = _wrap_jagged_dim(inp.dim(), new_kwargs["dim"], "split")
return tuple(
NestedTensor(values=x, **extract_kwargs(inp))
for x in func(inp._values, **new_kwargs)
)
@register_jagged_func(
torch.ops.aten.split_with_sizes.default, "self: jt, split_sizes: any, dim: any"
)
def split_with_sizes_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
new_kwargs["dim"] = _wrap_jagged_dim(
inp.dim(), new_kwargs["dim"], "split_with_sizes"
)
return [
NestedTensor(values=x, **extract_kwargs(inp))
for x in func(inp._values, **new_kwargs)
]
@register_jagged_func(torch.ops.aten.chunk.default, "self: jt, chunks: any, dim: any?")
def chunk_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
new_kwargs["dim"] = _wrap_jagged_dim(
inp.dim(), new_kwargs["dim"], "chunk", allow_batch_dim=True
)
if new_kwargs["dim"] == 0:
chunks = new_kwargs["chunks"]
dim0_size = inp._size[0]
chunk_size = math.ceil(dim0_size / chunks)
# get _offsets of the chunks
lengths = inp._offsets.diff()
chunked_lengths = lengths.chunk(chunks)
chunked_offsets = [torch.cumsum(x, dim=0) for x in chunked_lengths]
chunked_offsets = [F.pad(x, (1, 0), value=0) for x in chunked_offsets]
nested_kwargs = [
{"offsets": per_offsets, "_ragged_idx": inp._ragged_idx}
for per_offsets in chunked_offsets
]
# get _values of the chunks
split_sizes = [x.sum().item() for x in chunked_lengths]
chunk_values = inp._values.split(split_sizes)
return [
NestedTensor(values=chunk_values[i], **(nested_kwargs[i]))
for i in range(0, chunk_size)
]
else:
return [
NestedTensor(values=x, **extract_kwargs(inp))
for x in func(inp._values, **new_kwargs)
]
@register_jagged_func(torch.ops.aten.unbind.int, "self: jt_all, dim: any?")
def unbind_int(func, *args, **kwargs):
# Note that this specializes on the length of the offsets
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
dim = new_kwargs["dim"]
if dim != 0:
raise RuntimeError("unbind(): only supported for NestedTensor on dim=0")
inp = new_kwargs.pop("input")
values = inp.values()
offsets = inp.offsets()
lengths = inp.lengths()
if inp._ragged_idx != 1:
raise RuntimeError(
"unbind(): only supported for NestedTensor when jagged dimension is 1"
)
if lengths is None:
return torch.split(values, offsets.diff().tolist())
return [
values[offsets[i] : (offsets[i] + lengths[i])] for i in range(lengths.shape[0])
]
@register_jagged_func(torch.ops.aten.squeeze.dim, "self: jt, dim: any")
def squeeze_dim(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
values = inp._values
new_kwargs["dim"] = _wrap_jagged_dim(len(inp._size), new_kwargs["dim"], "squeeze")
return NestedTensor(func(values, **new_kwargs), **extract_kwargs(inp))
@register_jagged_func(torch.ops.aten.unsqueeze.default, "self: jt, dim: any")
def unsqueeze_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
values = inp._values
# Account for collapsed jagged dim
dim = new_kwargs["dim"]
new_kwargs["dim"] = _wrap_jagged_dim(len(inp._size) + 1, dim, "unsqueeze")
return NestedTensor(func(values, **new_kwargs), **extract_kwargs(inp))
@register_jagged_func(torch.ops.aten.cat.default, "tensors: any, dim: any")
def cat_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
tensors = new_kwargs.pop("tensors")
# Convert any non-nested to nested
nested = [t for t in tensors if t.is_nested]
assert len(nested) > 0
first = nested[0]
tensors = [t if t.is_nested else t.expand_as(first) for t in tensors]
# Account for collapsed jagged dim
dim = new_kwargs["dim"]
new_kwargs["dim"] = _wrap_jagged_dim(len(first.shape), dim, "cat")
return NestedTensor(
func([t._values for t in tensors], **new_kwargs), **extract_kwargs(tensors[0])
)
@register_jagged_func(torch.ops.aten.matmul.default, "self: jt, other: any")
def matmul_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
other = new_kwargs.pop("other")
if inp.is_nested and not other.is_nested:
return NestedTensor(
func(inp._values, other, **new_kwargs), **extract_kwargs(inp)
)
elif inp.is_nested and other.is_nested:
# BMM with equivalent ragged dims between the two inputs
if inp.dim() > 3 and other.dim() > 3 and raggedness_matches(inp, other._size):
return NestedTensor(func(inp._values, other._values), **extract_kwargs(inp))
raise RuntimeError(
f"matmul(): not supported between inputs of shapes {inp._size} and {other.shape}"
)
@register_jagged_func(
torch.ops.aten.expand.default, "self: jt, size: any, implicit: any?"
)
def expand_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
size = new_kwargs["size"]
assert ("implicit" not in new_kwargs) or (not new_kwargs.pop("implicit"))
if not raggedness_matches(inp, size):
raise RuntimeError(f"expand(): cannot expand shape {inp._size} -> {size}")
expand_arg = [-1, *size[2:]]
return NestedTensor(func(inp._values, expand_arg), **extract_kwargs(inp))
@register_jagged_func(torch.ops.aten.expand_as.default, "self: t, other: jt")
def expand_as_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
other = new_kwargs.pop("other")
return NestedTensor(func(inp, other._values), **extract_kwargs(other))
@register_jagged_func(torch.ops.aten.where.self, "condition: jt, self: jt, other: jt")
def where_self(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
condition = new_kwargs.pop("condition")
inp = new_kwargs.pop("input")
other = new_kwargs.pop("other")
assert condition._size == other._size == inp._size
return NestedTensor(
func(condition._values, inp._values, other._values, **new_kwargs),
**extract_kwargs(condition),
)
@register_jagged_func(torch.ops.aten._pin_memory.default, "self: jt, device: any?")
def _pin_memory_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
@register_jagged_func(torch.ops.aten.is_pinned.default, "self: jt, device: any?")
def is_pinned_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
return func(inp._values, **new_kwargs)
@register_jagged_func(
torch.ops.aten.is_same_size.default, "self: jt_all, other: jt_all"
)
def is_same_size_default(func, *args, **kwargs):
return args[0]._size == args[1]._size
@register_jagged_func(
torch.ops.aten.sum.dim_IntList, "self: jt, dim: any?, keepdim: any?, dtype: any?"
)
def sum_dim_IntList(func, *args, **kwargs):
# sum_dim_IntList can produce a NT or a T depending on whether the ragged dims
# are reduced away.
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
assert inp._ragged_idx == 1
new_kwargs["dim"], ragged_reduced_away = _wrap_jagged_dims(
inp.dim(), new_kwargs["dim"], "sum"
)
if not ragged_reduced_away:
return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
else:
# Don't wrap because we reduced away the raggedness
out = func(inp._values, **new_kwargs)
if new_kwargs["keepdim"]:
out = out.unsqueeze(0)
return out
@register_jagged_func(
torch.ops.aten.transpose.int, "self: jt_all, dim0: any, dim1: any"
)
def transpose_int(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
from torch._prims_common import canonicalize_dims
inp = new_kwargs.pop("input")
dim0, dim1 = canonicalize_dims(inp.dim(), (new_kwargs["dim0"], new_kwargs["dim1"]))
if inp._lengths is not None:
raise ValueError(
"transpose(): not supported on jagged layout nested tensor with holes"
)
# To support the SDPA API, inputs need to have the ragged idx transposed to dim 2
# instead of 1, although the internal Flash and mem-effn implementations will
# use the inputs with raggedness in dim 1.
if dim0 == inp._ragged_idx or dim1 == inp._ragged_idx:
if dim0 == 0 or dim1 == 0:
raise ValueError(
"Transpose is not supported on the batch dimension for jagged NT"
)
if dim0 == inp._ragged_idx:
to_dim = dim1
else:
to_dim = dim0
inp_kwargs = extract_kwargs(inp)
inp_kwargs["_ragged_idx"] = to_dim
return NestedTensor(
inp.values().transpose(
_outer_to_inner_dim(len(inp._size), dim0),
_outer_to_inner_dim(len(inp._size), dim1),
),
**inp_kwargs,
)
new_kwargs["dim0"] = _wrap_jagged_dim(inp.dim(), new_kwargs["dim0"], "transpose")
new_kwargs["dim1"] = _wrap_jagged_dim(inp.dim(), new_kwargs["dim1"], "transpose")
return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
@register_jagged_func(
[torch.ops.aten.view.default, torch.ops.aten._unsafe_view.default],
"self: jt_all, size: any",
)
def view_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
size = new_kwargs.pop("size")
if inp._ragged_idx != 1 and tuple(inp._size) != tuple(size):
raise RuntimeError(
f"view(): does not support ragged_idx != 1 except when inp._size == size. "
f"inp._size is ({inp._size}) and size is ({size})."
)
# Ensure specified size still includes batch and ragged dims
if len(size) < 3 or not raggedness_matches(inp, size):
raise RuntimeError(f"view(): cannot view shape {inp._size} as {size}")
# outer size: the size of the NT, e.g. [3, j0, 10]
# inner size: the size of the values, e.g. [8, 10] (e.g. for offsets = [0, 3, 5, 8])
# this function gets inner_size[inner_idx] for a given inner_idx.
#
# example: for outer size [a, b, c, j0, d, e, f]
# assume that j0 is ragged, other are concrete integers
# and ragged_idx=3
# inner size will be [b, c, inp._values.size(ragged_idx), d, e, f]
# therefore:
# inner_size[0] = outer_size[1]
# inner_size[1] = outer_size[2]
# inner_size[0] = inp._values.size(ragged_idx - 1)
# inner_size[3] = outer_size[4]
# inner_size[4] = outer_size[5]
def get_inner_size(inner_idx):
nonlocal inp, size
if inner_idx == inp._ragged_idx - 1:
return inp._values.size(inner_idx)
else:
return size[inner_idx + 1]
inner_size = [get_inner_size(i) for i in range(len(size) - 1)]
return NestedTensor(func(inp._values, inner_size), **extract_kwargs(inp))
@register_jagged_func(
torch.ops.aten.native_layer_norm.default,
"input: jt, normalized_shape: any, weight: any?, bias: any?, eps: any",
)
def native_layer_norm_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
normalized_shape = new_kwargs["normalized_shape"]
# Ensure we're not trying to normalize over the ragged dim
if inp.dim() < 3 or (inp.dim() - len(normalized_shape)) < 2:
raise RuntimeError(
"layer_norm(): normalizing over ragged dim not supported for nested tensors"
)
output, mean, std = func(inp._values, **new_kwargs)
return (NestedTensor(output, **extract_kwargs(inp)), mean, std)
@register_jagged_func(
torch.ops.aten.native_layer_norm_backward.default,
"grad_out: jt, input: jt, normalized_shape: any, mean: any, rstd: any, weight: any?, bias: any?, output_mask: any",
)
def native_layer_norm_backward_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
grad_out = new_kwargs.pop("grad_out")
inp = new_kwargs.pop("input")
d_input, d_gamma, d_beta = func(grad_out._values, inp._values, **new_kwargs)
if d_input is None:
return (None, d_gamma, d_beta)
return (NestedTensor(d_input, **extract_kwargs(inp)), d_gamma, d_beta)
@register_jagged_func(torch.ops.aten.select.int, "self: jt, dim: any, index: any")
def select_int(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
new_kwargs["dim"] = _wrap_jagged_dim(inp.dim(), new_kwargs["dim"], "select")
return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
@register_jagged_func(
torch.ops.aten.slice.Tensor,
"self: jt, dim: any?, start: any?, end: any?, step: any?",
)
def slice_tensor(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
new_kwargs["dim"] = _wrap_jagged_dim(inp.dim(), new_kwargs["dim"], "slice")
return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
@register_jagged_func(
torch.ops.aten.convolution.default,
"input: jt, weight: t, bias: t?, stride: any, padding: any, "
"dilation: any, transposed: any, output_padding: any, groups: any",
)
def convolution_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
@register_jagged_func(
torch.ops.aten.mean.dim, "self: jt, dim: any?, keepdim: any, dtype: any?"
)
def mean_dim(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
# NB: mean expects dim as a single item list of ints for some reason
new_kwargs["dim"] = [_wrap_jagged_dim(inp.dim(), new_kwargs["dim"][0], "mean")]
return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp))
@register_jagged_func(torch.ops.aten.stack.default, "tensors: any, dim: any")
def stack_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
# guaranteed this is non-empty if we got here
tensors = new_kwargs.pop("tensors")
for t in tensors:
if not isinstance(t, NestedTensor):
raise RuntimeError("stack(): expected all nested tensors inputs")
if t.dim() != tensors[0].dim():
raise RuntimeError(
"stack(): expected all nested tensors to have the same dim"
)
if not raggedness_matches(t, tensors[0].shape):
raise RuntimeError(
"stack(): expected all nested tensors to have the same nested structure"
)
new_kwargs["dim"] = _wrap_jagged_dim(
tensors[0].dim() + 1, new_kwargs["dim"], "stack"
)
return NestedTensor(
func([t._values for t in tensors], **new_kwargs), **extract_kwargs(tensors[0])
)
@register_jagged_func(
torch.ops.aten.embedding.default,
"weight: t, indices: jt, padding_idx: any?, scale_grad_by_freq: any?, sparse: any?",
)
def embedding_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
# guaranteed this is non-empty if we got here
indices = new_kwargs.pop("indices")
weight = new_kwargs.pop("weight")
return NestedTensor(
func(weight, indices._values, **new_kwargs), **extract_kwargs(indices)
)
@register_jagged_func(
[
torch.ops.aten.values.default,
torch.ops.aten._nested_get_values.default,
],
"self: jt_all",
)
def values_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
# TODO: Handle inference mode properly.
# See https://github.com/pytorch/pytorch/issues/112024#issuecomment-1779554292
return inp._values.detach()
@register_jagged_func(
torch.ops.aten._nested_view_from_jagged.default,
"values: t, offsets: t, dummy: jt_all, lengths: t?, ragged_idx: any?",
)
def _nested_view_from_jagged_default(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
values, offsets, lengths = (
new_kwargs["input"],
new_kwargs["offsets"],
new_kwargs["lengths"],
)
ragged_idx = new_kwargs["ragged_idx"]
return NestedTensor(values, offsets, lengths=lengths, _ragged_idx=ragged_idx)
@register_jagged_func(torch.ops.aten._nested_get_offsets.default, "self: jt_all")
def _nested_get_offsets(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
return inp._offsets
@register_jagged_func(torch.ops.aten._nested_get_lengths.default, "self: jt_all")
def _nested_get_lengths(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
return inp._lengths
@register_jagged_func(torch.ops.aten._nested_get_ragged_idx.default, "self: jt_all")
def _nested_get_ragged_idx(func, *args, **kwargs):
_, new_kwargs = normalize_function(
func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True
)
inp = new_kwargs.pop("input")
return inp._ragged_idx
# Make the dummy available on the C++ side.
@register_jagged_func(torch.ops.aten._nested_get_jagged_dummy.default, "self: any")
def _nested_get_jagged_dummy(func, *args, **kwargs):
from torch.nested._internal.nested_tensor import _nt_view_dummy
return _nt_view_dummy
with torch.library._scoped_library("aten", "IMPL") as aten:
aten.impl("_nested_get_jagged_dummy", _nested_get_jagged_dummy, "CPU")
aten.impl("_nested_get_jagged_dummy", _nested_get_jagged_dummy, "CUDA")
aten.impl("_nested_get_jagged_dummy", _nested_get_jagged_dummy, "Meta")
|