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db.py
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db.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import specdb.dtypes as dt
import specdb.function as fn
import torch
from inputgen.argument.type import ArgType
from inputgen.specs.model import (
ConstraintProducer as cp,
InKwArg,
InPosArg,
OutArg,
Spec,
)
from inputgen.variable.type import ScalarDtype
from specdb.default import (
DimDefault,
DimListDefault,
IndexDefault,
MemoryFormatDefault,
ShapeDefault,
)
SpecDB = [
Spec(
op="_cdist_forward.default", # (Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="x1",
constraints=[
cp.Dtype.In(lambda deps: dt._floating),
cp.Rank.Ge(lambda deps: 2),
],
),
InPosArg(
ArgType.Tensor,
name="x2",
deps=[0],
constraints=[
cp.Dtype.Eq(lambda deps: deps[0].dtype),
cp.Rank.Ge(lambda deps: 2),
cp.Size.Eq(
lambda deps, r, d: (
fn.safe_size(deps[0], deps[0].dim() - 1)
if d == r - 1
else None
)
),
cp.Size.In(
lambda deps, r, d: (
fn.broadcast_with(deps[0].shape[:-2], r - 2, d)
if d < r - 2
else None
)
),
],
),
InPosArg(
ArgType.Float,
name="p",
constraints=[
cp.Value.Ge(lambda deps: 0),
cp.Value.Be(
lambda deps: [
0,
0.1,
0.5,
1,
1.5,
2,
2.5,
3,
5,
10,
float("inf"),
]
),
],
),
InPosArg(
ArgType.IntOpt,
name="compute_mode",
constraints=[
cp.Value.In(lambda deps: [0, 1, 2]),
],
),
],
outspec=[
OutArg(
ArgType.Tensor,
constraints=[
cp.Dtype.Eq(lambda deps: deps[0].dtype),
],
)
],
),
Spec( # TODO(mcandales): Calibrate.
op="_log_softmax.default", # (Tensor self, int dim, bool half_to_float) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: dt._floating),
],
),
InPosArg(
ArgType.Dim,
name="dim",
deps=[0],
constraints=DimDefault,
),
InPosArg(
ArgType.Bool,
name="half_to_float",
# TODO(mcandales): CPU specific constraint
constraints=[
cp.Value.Eq(lambda deps: False),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec( # TODO(mcandales): Calibrate ATen's implementation.
op="_native_batch_norm_legit_no_training.default", # (Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor)
inspec=[
InPosArg(
ArgType.Tensor,
name="input",
constraints=[
cp.Rank.Ge(lambda deps: 2),
],
),
InPosArg(
ArgType.TensorOpt,
name="weight",
deps=[0],
constraints=[
cp.Rank.Eq(lambda deps: 1),
cp.Size.Eq(lambda deps, r, d: fn.safe_size(deps[0], 1)),
],
),
InPosArg(
ArgType.TensorOpt,
name="bias",
deps=[0],
constraints=[
cp.Rank.Eq(lambda deps: 1),
cp.Size.Eq(lambda deps, r, d: fn.safe_size(deps[0], 1)),
],
),
InPosArg(
ArgType.Tensor,
name="running_mean",
deps=[0],
constraints=[
cp.Rank.Eq(lambda deps: 1),
cp.Size.Eq(lambda deps, r, d: fn.safe_size(deps[0], 1)),
],
),
InPosArg(
ArgType.Tensor,
name="running_var",
deps=[0],
constraints=[
cp.Rank.Eq(lambda deps: 1),
cp.Size.Eq(lambda deps, r, d: fn.safe_size(deps[0], 1)),
],
),
InPosArg(ArgType.Float, name="momentum"),
InPosArg(ArgType.Float, name="eps"),
],
outspec=[
OutArg(ArgType.Tensor, name="out0"),
OutArg(ArgType.Tensor, name="out1"),
OutArg(ArgType.Tensor, name="out2"),
],
),
Spec(
op="_pdist_forward.default", # (Tensor self, float p=2) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: dt._floating),
cp.Rank.Eq(lambda deps: 2),
],
),
InPosArg(
ArgType.Float,
name="p",
constraints=[
cp.Value.Ge(lambda deps: 0),
cp.Value.Be(
lambda deps: [
0,
0.1,
0.5,
1,
1.5,
2,
2.5,
3,
5,
10,
float("inf"),
]
),
],
),
],
outspec=[
OutArg(
ArgType.Tensor,
constraints=[
cp.Dtype.Eq(lambda deps: deps[0].dtype),
],
)
],
),
Spec( # TODO(mcandales): Calibrate.
op="_softmax.default", # (Tensor self, int dim, bool half_to_float) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: dt._floating),
],
),
InPosArg(
ArgType.Dim,
name="dim",
deps=[0],
constraints=DimDefault,
),
InPosArg(
ArgType.Bool,
name="half_to_float",
# TODO(mcandales): CPU specific constraint
constraints=[
cp.Value.Eq(lambda deps: False),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="_to_copy.default", # (Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
InKwArg(
ArgType.Bool,
name="non_blocking",
# TODO(mcandales): Executorch specific constraint
# constraints=[cp.Value.In(lambda deps: [False])],
),
InKwArg(ArgType.ScalarTypeOpt, name="dtype"),
InKwArg(
ArgType.MemoryFormat,
name="memory_format",
constraints=MemoryFormatDefault,
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="abs.default", # (Tensor self) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.Ne(lambda deps: torch.bool),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="acos.default", # (Tensor self) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="acosh.default", # (Tensor self) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="add.Tensor", # (Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
InPosArg(
ArgType.Tensor,
name="other",
deps=[0],
constraints=[
cp.Size.In(
lambda deps, r, d: fn.broadcast_with(deps[0].shape, r, d)
),
],
),
InKwArg(
ArgType.Scalar,
name="alpha",
deps=[0, 1],
constraints=[
cp.Dtype.In(
lambda deps: fn.add_alpha_st(
fn.dt_to_st(
torch.promote_types(deps[0].dtype, deps[1].dtype)
)
)
),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="add.Scalar", # (Tensor self, Scalar other, Scalar alpha=1) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
InPosArg(ArgType.Scalar, name="other"),
InPosArg(
ArgType.Scalar,
name="alpha",
deps=[0, 1],
constraints=[
cp.Dtype.Ne(
lambda deps: (
ScalarDtype.bool
if deps[0].dtype != torch.bool
or deps[1] != ScalarDtype.bool
else None
)
),
cp.Dtype.In(
lambda deps: fn.st_le(
fn.dt_to_st(
fn.promote_type_with_scalar(deps[0].dtype, deps[1])
)
)
),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="addmm.default", # (Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
deps=[1, 2],
constraints=[
cp.Dtype.Eq(lambda deps: deps[0].dtype),
cp.Rank.Le(lambda deps: 2),
cp.Size.In(
lambda deps, r, d: fn.broadcast_to(
(fn.safe_size(deps[0], 0), fn.safe_size(deps[1], 1)), r, d
)
),
],
),
InPosArg(
ArgType.Tensor,
name="mat1",
constraints=[
cp.Dtype.Ne(lambda deps: torch.bool),
cp.Rank.Eq(lambda deps: 2),
],
),
InPosArg(
ArgType.Tensor,
name="mat2",
deps=[1],
constraints=[
cp.Dtype.Eq(lambda deps: deps[0].dtype),
cp.Rank.Eq(lambda deps: 2),
cp.Size.Eq(
lambda deps, r, d: fn.safe_size(deps[0], 1) if d == 0 else None
),
],
),
InKwArg(
ArgType.Scalar,
name="beta",
deps=[1],
constraints=[
cp.Value.Ge(
lambda deps, dtype: fn.dtype_lower_bound(deps[0].dtype)
),
cp.Value.Le(
lambda deps, dtype: fn.dtype_upper_bound(deps[0].dtype)
),
],
),
InKwArg(
ArgType.Scalar,
name="alpha",
deps=[1],
constraints=[
cp.Value.Ge(
lambda deps, dtype: fn.dtype_lower_bound(deps[0].dtype)
),
cp.Value.Le(
lambda deps, dtype: fn.dtype_upper_bound(deps[0].dtype)
),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="alias_copy.default", # (Tensor self) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
],
outspec=[OutArg(ArgType.Tensor)],
),
Spec(
op="amax.default", # (Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
InPosArg(
ArgType.DimList,
name="dim",
deps=[0],
constraints=[
cp.Length.Ge(lambda deps: 1 if deps[0].numel() == 0 else 0),
cp.Value.Gen(
lambda deps, length: (
fn.valid_dim_list_non_zero_size(deps[0], length),
fn.invalid_dim_list_non_zero_size(deps[0], length),
)
),
],
),
InPosArg(ArgType.Bool, name="keepdim"),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="amin.default", # (Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
InPosArg(
ArgType.DimList,
name="dim",
deps=[0],
constraints=[
cp.Length.Ge(lambda deps: 1 if deps[0].numel() == 0 else 0),
cp.Value.Gen(
lambda deps, length: (
fn.valid_dim_list_non_zero_size(deps[0], length),
fn.invalid_dim_list_non_zero_size(deps[0], length),
)
),
],
),
InPosArg(ArgType.Bool, name="keepdim"),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="any.default", # (Tensor self) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
],
outspec=[
OutArg(
ArgType.Tensor,
constraints=[
cp.Dtype.In(lambda deps: [torch.bool, torch.uint8]),
],
),
],
),
Spec(
op="any.dim", # (Tensor self, int dim, bool keepdim=False) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
InPosArg(
ArgType.Dim,
name="dim",
deps=[0],
constraints=DimDefault,
),
InPosArg(ArgType.Bool, name="keepdim"),
],
outspec=[
OutArg(
ArgType.Tensor,
constraints=[
cp.Dtype.In(lambda deps: [torch.bool, torch.uint8]),
],
),
],
),
Spec(
op="any.dims", # (Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
InPosArg(
ArgType.DimListOpt,
name="dim",
deps=[0],
constraints=DimListDefault,
),
InPosArg(ArgType.Bool, name="keepdim"),
],
outspec=[
OutArg(
ArgType.Tensor,
constraints=[
cp.Dtype.In(lambda deps: [torch.bool, torch.uint8]),
],
),
],
),
Spec( # TODO(mcandales): Calibrate.
op="arange.default", # (Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inspec=[
InPosArg(
ArgType.Scalar,
name="end",
constraints=[
cp.Value.Ge(lambda deps, dtype: 0),
cp.Value.NotIn(lambda deps, dtype: [float("-inf"), float("inf")]),
],
),
InKwArg(
ArgType.ScalarTypeOpt,
name="dtype",
constraints=[
cp.Value.Ne(lambda deps: torch.bool),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec( # TODO(mcandales): Calibrate.
op="arange.start_step", # (Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inspec=[
InPosArg(
ArgType.Scalar,
name="start",
constraints=[
cp.Value.NotIn(lambda deps, dtype: [float("-inf"), float("inf")]),
],
),
InPosArg(
ArgType.Scalar,
name="end",
constraints=[
cp.Value.NotIn(lambda deps, dtype: [float("-inf"), float("inf")]),
],
),
InPosArg(
ArgType.Scalar,
name="step",
deps=[0, 1],
constraints=[
cp.Value.Gt(lambda deps, dtype: 0 if deps[0] < deps[1] else None),
cp.Value.Lt(lambda deps, dtype: 0 if deps[0] > deps[1] else None),
],
),
InKwArg(
ArgType.ScalarTypeOpt,
name="dtype",
constraints=[
cp.Value.Ne(lambda deps: torch.bool),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec( # TODO(mcandales): Calibrate.
op="argmax.default", # (Tensor self, int? dim=None, bool keepdim=False) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.Ne(lambda deps: torch.bool),
],
),
InPosArg(
ArgType.DimOpt,
name="dim",
deps=[0],
constraints=[
cp.Optional.Eq(
lambda deps: False if deps[0].numel() == 0 else None
),
cp.Value.In(lambda deps: fn.dim_non_zero_size(deps[0])),
],
),
InPosArg(ArgType.Bool, name="keepdim"),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec( # TODO(mcandales): Calibrate.
op="argmin.default", # (Tensor self, int? dim=None, bool keepdim=False) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
# TODO(mcandales): Enable self-context
# cp.Dtype.Ne(lambda deps, ctx: torch.bool if not ctx.Empty else None),
cp.Dtype.Ne(lambda deps: torch.bool),
],
),
InPosArg(
ArgType.DimOpt,
name="dim",
deps=[0],
constraints=[
cp.Optional.Eq(
lambda deps: False if deps[0].numel() == 0 else None
),
cp.Value.In(lambda deps: fn.dim_non_zero_size(deps[0])),
],
),
InPosArg(ArgType.Bool, name="keepdim"),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec( # TODO(mcandales) Implement numel constraint prefix
op="as_strided_copy.default", # (Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
deps=[1, 2, 3],
constraints=[
cp.Rank.Ge(
lambda deps: (
1
if fn.as_strided_min_numel(deps[0], deps[1], deps[2]) > 1
else 0
)
),
cp.Size.Gen(
lambda deps, rank: (
fn.valid_as_strided_sizes(deps[0], deps[1], deps[2], rank),
fn.invalid_as_strided_sizes(
deps[0], deps[1], deps[2], rank
),
)
),
],
),
InPosArg(
ArgType.Shape,
name="size",
constraints=ShapeDefault,
),
InPosArg(
ArgType.LengthList,
name="stride",
deps=[1],
constraints=[
cp.Length.Eq(lambda deps: len(deps[0])),
cp.Value.Ge(lambda deps, length, ix: 0),
],
),
InPosArg(
ArgType.LengthOpt,
name="storage_offset",
constraints=[
cp.Value.Ge(lambda deps: 0),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="asin.default", # (Tensor self) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="asinh.default", # (Tensor self) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="atan.default", # (Tensor self) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="atan2.default", # (Tensor self, Tensor other) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
InPosArg(
ArgType.Tensor,
name="other",
deps=[0],
constraints=[
cp.Size.In(
lambda deps, r, d: fn.broadcast_with(deps[0].shape, r, d)
),
],
),
],
outspec=[
OutArg(
ArgType.Tensor,
constraints=[
cp.Dtype.In(lambda deps: dt.can_cast_from(torch.float)),
cp.Dtype.In(
lambda deps: dt.can_cast_from(
torch.promote_types(deps[0].dtype, deps[1].dtype)
)
),
],
)
],
),
Spec(
op="atanh.default", # (Tensor self) -> Tensor
inspec=[
InPosArg(ArgType.Tensor, name="self"),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec( # TODO(mcandales) Add input constraints to ensure valid output shape
op="avg_pool2d.default", # (Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor
inspec=[
InPosArg( # self
ArgType.Tensor,
name="self",
deps=[1, 2, 3, 4],
constraints=[
cp.Dtype.In(lambda deps: dt._floating + [torch.long]),
cp.Rank.In(lambda deps: [3, 4]),
cp.Size.Ge(
lambda deps, r, d: fn.pool_input_size_min(
2, deps[0], deps[1], deps[2], [], deps[3], r, d
)
),
],
),
InPosArg( # kernel_size
ArgType.LengthList,
name="kernel_size",
constraints=[
cp.Length.In(lambda deps: [1, 2]),
cp.Value.Ge(lambda deps, length, ix: 1),
],
),
InPosArg( # stride
ArgType.LengthList,
name="stride",
constraints=[
cp.Length.In(lambda deps: [0, 1, 2]),
cp.Value.Ge(lambda deps, length, ix: 1),
],
),
InPosArg( # padding
ArgType.LengthList,
name="padding",
deps=[1],
constraints=[
cp.Length.In(lambda deps: [1, 2]),
cp.Value.Ge(lambda deps, length, ix: 0),
cp.Value.Le(
lambda deps, length, ix: fn.pool_padding_max(
deps[0], length, ix
)
),
],
),
InPosArg(ArgType.Bool, name="ceil_mode"), # ceil_mode
InPosArg(ArgType.Bool, name="count_include_pad"), # count_include_pad
InPosArg( # divisor_override
ArgType.IntOpt,
name="divisor_override",
constraints=[
cp.Value.Ne(lambda deps: 0),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="bitwise_and.Scalar", # (Tensor self, Scalar other) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: dt._int_and_bool),
],
),
InPosArg(
ArgType.Scalar,
name="other",
constraints=[
cp.Dtype.In(lambda deps: [ScalarDtype.bool, ScalarDtype.int]),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="bitwise_and.Tensor", # (Tensor self, Tensor other) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: dt._int_and_bool),
],
),
InPosArg(
ArgType.Tensor,
name="other",
deps=[0],
constraints=[
cp.Dtype.In(lambda deps: dt._int_and_bool),
cp.Size.In(
lambda deps, r, d: fn.broadcast_with(deps[0].shape, r, d)
),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="bitwise_not.default", # (Tensor self) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: dt._int_and_bool),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="bitwise_or.Scalar", # (Tensor self, Scalar other) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: dt._int_and_bool),
],
),
InPosArg(
ArgType.Scalar,
name="other",
constraints=[
cp.Dtype.In(lambda deps: [ScalarDtype.bool, ScalarDtype.int]),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="bitwise_or.Tensor", # (Tensor self, Tensor other) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: dt._int_and_bool),
],
),
InPosArg(
ArgType.Tensor,
name="other",
deps=[0],
constraints=[
cp.Dtype.In(lambda deps: dt._int_and_bool),
cp.Size.In(
lambda deps, r, d: fn.broadcast_with(deps[0].shape, r, d)
),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="bitwise_xor.Scalar", # (Tensor self, Scalar other) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: dt._int_and_bool),
],
),
InPosArg(
ArgType.Scalar,
name="other",
constraints=[
cp.Dtype.In(lambda deps: [ScalarDtype.bool, ScalarDtype.int]),
],
),
],
outspec=[
OutArg(ArgType.Tensor),
],
),
Spec(
op="bitwise_xor.Tensor", # (Tensor self, Tensor other) -> Tensor
inspec=[
InPosArg(
ArgType.Tensor,
name="self",
constraints=[
cp.Dtype.In(lambda deps: dt._int_and_bool),
],
),
InPosArg(
ArgType.Tensor,
name="other",
deps=[0],
constraints=[
cp.Dtype.In(lambda deps: dt._int_and_bool),