93 lines
3.7 KiB
Python
93 lines
3.7 KiB
Python
from typing import Any, List, Literal, Union, overload, Sequence
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from jax._src.typing import (
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Array, ArrayLike, DTypeLike
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)
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from jax._src.core import AxisName
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from jax._src.cudnn.scaled_matmul_stablehlo import BlockScaleConfig
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from jax._src.lax.lax import DotDimensionNumbers
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from jax.nn import initializers as initializers
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_Axis = Union[None, int, Sequence[int]]
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def celu(x: ArrayLike, alpha: ArrayLike = ...) -> Array: ...
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def dot_product_attention(
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query: ArrayLike,
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key: ArrayLike,
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value: ArrayLike,
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bias: ArrayLike | None = ...,
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mask: ArrayLike | None = ...,
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*,
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scale: float | None = ...,
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is_causal: bool = ...,
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query_seq_lengths: ArrayLike | None = ...,
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key_value_seq_lengths: ArrayLike | None = ...,
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local_window_size: int | tuple[int, int] | None = ...,
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implementation: Literal['xla', 'cudnn'] | None = ...) -> Array: ...
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def elu(x: ArrayLike, alpha: ArrayLike = ...) -> Array: ...
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def gelu(x: ArrayLike, approximate: bool = ...) -> Array: ...
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def get_scaled_dot_general_config(mode: Literal['nvfp4', 'mxfp8'],
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global_scale: Array | None = ...) -> BlockScaleConfig: ...
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def glu(x: ArrayLike, axis: int = ...) -> Array: ...
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def hard_sigmoid(x: ArrayLike) -> Array: ...
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def hard_silu(x: ArrayLike) -> Array: ...
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def hard_swish(x: ArrayLike) -> Array: ...
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def hard_tanh(x: ArrayLike) -> Array: ...
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def identity(x: ArrayLike) -> Array: ...
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def leaky_relu(x: ArrayLike, negative_slope: ArrayLike = ...) -> Array: ...
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def log_sigmoid(x: ArrayLike) -> Array: ...
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def log_softmax(x: ArrayLike,
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axis: int | tuple[int, ...] | None = ...,
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where: ArrayLike | None = ...) -> Array: ...
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@overload
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def logsumexp(a: ArrayLike, axis: _Axis = ..., b: ArrayLike | None = ...,
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keepdims: bool = ..., return_sign: Literal[False] = ..., where: ArrayLike | None = ...) -> Array: ...
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@overload
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def logsumexp(a: ArrayLike, axis: _Axis = ..., b: ArrayLike | None = ...,
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keepdims: bool = ..., *, return_sign: Literal[True], where: ArrayLike | None = ...) -> tuple[Array, Array]: ...
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@overload
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def logsumexp(a: ArrayLike, axis: _Axis = ..., b: ArrayLike | None = ...,
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keepdims: bool = ..., return_sign: bool = ..., where: ArrayLike | None = ...) -> Array | tuple[Array, Array]: ...
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def mish(x: ArrayLike) -> Array: ...
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def one_hot(x: Any, num_classes: int, *,
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dtype: Any = ..., axis: int | AxisName = ...) -> Array: ...
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def relu(x: ArrayLike) -> Array: ...
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def relu6(x: ArrayLike) -> Array: ...
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def scaled_dot_general(
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lhs: ArrayLike, rhs: ArrayLike,
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dimension_numbers: DotDimensionNumbers,
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preferred_element_type: DTypeLike = ...,
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configs: List[BlockScaleConfig] | None = ...,
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implementation: Literal['cudnn'] | None = ...,
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) -> Array: ...
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def scaled_matmul(
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lhs: Array,
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rhs: Array,
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lhs_scales: Array,
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rhs_scales: Array,
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preferred_element_type: DTypeLike = ...,
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) -> Array: ...
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def selu(x: ArrayLike) -> Array: ...
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def sigmoid(x: ArrayLike) -> Array: ...
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def silu(x: ArrayLike) -> Array: ...
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def soft_sign(x: ArrayLike) -> Array: ...
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def softmax(x: ArrayLike,
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axis: int | tuple[int, ...] | None = ...,
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where: ArrayLike | None = ...) -> Array: ...
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def softplus(x: ArrayLike) -> Array: ...
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def sparse_plus(x: ArrayLike) -> Array: ...
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def sparse_sigmoid(x: ArrayLike) -> Array: ...
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def squareplus(x: ArrayLike, b: ArrayLike = ...) -> Array: ...
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def standardize(x: ArrayLike,
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axis: int | tuple[int, ...] | None = ...,
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mean: ArrayLike | None = ...,
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variance: ArrayLike | None = ...,
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epsilon: ArrayLike = ...,
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where: ArrayLike | None = ...) -> Array: ...
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def swish(x: ArrayLike) -> Array: ...
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def tanh(x: ArrayLike, /) -> Array: ...
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