| import functools |
| import math |
| |
| import torch |
| from torch.nested._internal.sdpa import jagged_scaled_dot_product_attention |
| |
| from .nested_tensor import NestedTensor |
| from typing import * # noqa: F403 |
| 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): |
| from torch._prims_common import canonicalize_dims |
| |
| wrapped = canonicalize_dims(ndim, dim) |
| if wrapped < 2: |
| raise RuntimeError( |
| f"{op_name}(): not supported for NestedTensor on dim=0 or dim=1" |
| ) |
| return _outer_to_inner_dim(ndim, 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 |
| |
| if not arg_type_check_fns[normalized_arg_type](args[i]): |
| type_to_desc = { |
| "t": "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, ...", 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, |
| } |
| 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") |
| new_kwargs["start_dim"] = _wrap_jagged_dim( |
| inp.dim(), new_kwargs["start_dim"], "flatten" |
| ) |
| new_kwargs["end_dim"] = _wrap_jagged_dim( |
| inp.dim(), new_kwargs["end_dim"], "flatten" |
| ) |
| |
| return NestedTensor(func(inp._values, **new_kwargs), **extract_kwargs(inp)) |
| |
| 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 |
| |
| # If jagged dim is not 1 it's not contiguous |
| if inp._ragged_idx != 1: |
| 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") |
| 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.ones_like.default, |
| torch.ops.aten.zeros_like.default, |
| torch.ops.aten.randn_like.default, |
| torch.ops.aten.detach.default, |
| ], |
| "self: jt", |
| )(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.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.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, other: jt") |
| 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 |
| return NestedTensor( |
| inp.values().transpose( |
| _outer_to_inner_dim(len(inp._size), dim0), |
| _outer_to_inner_dim(len(inp._size), dim1), |
| ), |
| **extract_kwargs(inp), |
| _ragged_idx=to_dim, |
| ) |
| |
| 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, "self: jt, 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") |
| |
| # 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}") |
| |
| jagged_size = [inp._values.shape[0]] + size[2:] |
| return NestedTensor(func(inp._values, jagged_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) |
| ) |
